Mail Code: 943054065
Web Site: https://statistics.stanford.edu/
Courses offered by the Department of Statistics are listed under the subject code STATS on the Stanford Bulletin's ExploreCourses web site.
The department's goals are to acquaint students with the role played in science and technology by probabilistic and statistical ideas and methods, to provide instruction in the theory and application of techniques that have been found to be commonly useful, and to train research workers in probability and statistics. There are courses for general students as well as those who plan careers in statistics in business, government, industry, and teaching.
The department has long recognized the relation of statistical theory to applications. It has fostered this by encouraging a liaison with other departments in the form of joint and courtesy faculty appointments, as well as membership in various interdisciplinary programs: Biomedical Data Science, BioX, Center for Computational, Evolutionary and Human Genomics, Computer Science, Economics, Education, Electrical Engineering, Environmental Earth System Science, Genetics, Mathematics, Mathematical and Computational Finance, and Medicine. The research activities of the department reflect an interest in applied and theoretical statistics and probability. There are workshops in biology/medicine and in environmental factors in health.
In addition to courses for Statistics students, the department offers a number of service courses designed for students in other departments. These tend to emphasize the application of statistical techniques rather than their theoretical development.
The department has always drawn visitors from other countries and universities, and as a result there are a wide range of seminars offered by both the visitors and the department's own faculty.
Undergraduate Programs in Statistics
The department offers a minor in Statistics and in Data Science. Program details can be found under the Minor section.
Undergraduates Interested in Statistics
Students wishing to build a concentration in probability and statistics are encouraged to consider declaring a major in Mathematical and Computational Science. This interdisciplinary program is administered in the Department of Statistics and provides core training in computing, mathematics, operations research, and statistics, with opportunities for further elective work and specialization. See the "Mathematical and Computational Science" section of this bulletin.
Graduate Programs in Statistics
University requirements for the M.S. and Ph.D. degrees are discussed in the "Graduate Degrees" section of this bulletin.
Learning Outcomes (Graduate)
The purpose of the master's program is to further develop knowledge and skills in Statistics and to prepare students for a professional career or doctoral studies. This is achieved through completion of courses, in the primary field as well as related areas, and experience with independent work and specialization.
The Ph.D. is conferred upon candidates who have demonstrated substantial scholarship and the ability to conduct independent research and analysis in Statistics. Through completion of advanced course work and rigorous skills training, the doctoral program prepares students to make original contributions to the knowledge of Statistics and to interpret and present the results of such research.
The Department of Statistics offers two minor programs for undergraduates, a minor in Data Science and a minor in Statistics. To declare either minor for a degree program, visit the Statistics website and submit the appropriate form to the department.
Minor in Data Science
The undergraduate Data Science minor has been designed for majors in the humanities and social sciences who want to gain practical knowledge of statistical data analytic methods as it relates to their field of interest. The minor:
 provides students with the knowledge of exploratory and confirmatory data analyses of diverse data types such as text, numbers, images, graphs, trees, and binary input)
 strengthens social research by teaching students how to correctly apply data analysis tools and the techniques of data visualization to convey their conclusions.
No previous programming or statistical background is assumed.
Learning Outcomes
Students are expected to:
 be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis.
 be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures
All courses for the minor must be taken for a letter grade, with the exception of the Data Mining requirement.
Seven courses are required, 22 units minimum. An overall 2.75 grade point average (GPA) is required for courses fulfilling the minor.
Requirements
Linear Algebra
Units  

One of the following:  
MATH 51  Linear Algebra, Multivariable Calculus, and Modern Applications  5 
CME 100  Vector Calculus for Engineers  5 
Programming
Units  

CS 106A  Programming Methodology  35 
Programming in R
Units  

One of the following:  
STATS 32  Introduction to R for Undergraduates  1 
STATS 195  Introduction to R  1 
THINK 3  Breaking Codes, Finding Patterns  4 
STATS 48N  Riding the Data Wave  3 
Or other course that teaches proficiency in R programming. 
Data Science
Units  

STATS 101  Data Science 101  5 
Statistics
Units  

One of the following:  
ECON 102A  Introduction to Statistical Methods (Postcalculus) for Social Scientists  5 
PHIL 166  Probability: Ten Great Ideas About Chance  4 
STATS 141  Biostatistics  35 
STATS 191  Introduction to Applied Statistics  34 
STATS 211  Metaresearch: Appraising Research Findings, Bias, and Metaanalysis  3 
STATS 216  Introduction to Statistical Learning  3 
Data Mining and Analysis
STATS 202  Data Mining and Analysis (may be taken CR/NC)  3 
Elective Course
Units  

One course fulfilling Data Science methodology from cognate field of interest. Suggested courses:  
CS 224W  Analysis of Networks  34 
ECON 291  Social and Economic Networks  25 
ENGLISH 184E  Literary Text Mining  5 
LINGUIST 275  Probability and Statistics for linguists  24 
MS&E 135  Networks  3 
PHIL 50S  3  
PHIL 166  Probability: Ten Great Ideas About Chance  4 
POLISCI 150B  Machine Learning for Social Scientists  5 
POLISCI 155  5  
POLISCI 450A  Political Methodology I: Regression  5 
PSYCH 109  An introduction to computation and cognition  4 
PUBLPOL 105  Empirical Methods in Public Policy  45 
SOC 126  Introduction to Social Networks  4 
SOC 180A  Foundations of Social Research  4 
or SOC 180B  Introduction to Data Analysis 
Minor in Statistics
The undergraduate minor in Statistics is designed to complement major degree programs primarily in the social and natural sciences. Students with an undergraduate Statistics minor should find broadened possibilities for employment. The Statistics minor provides valuable preparation for professional degree studies in postgraduate academic programs.
The minor consists of a minimum of six courses with a total of at least 20 units. There are two required courses (8 units) and four qualifying or elective courses (12 or more units). All courses for the minor must be taken for a letter grade. An overall 2.75 grade point average (GPA) is required for courses fulfilling the minor.
Required Courses
Units  

Both:  
STATS 116  Theory of Probability  35 
STATS 200  Introduction to Statistical Inference  3 
Qualifying Courses
At most, one of these two courses may be counted toward the six course requirement for the minor:
Units  

Choose one from the following:  
MATH 52  Integral Calculus of Several Variables  5 
STATS 191  Introduction to Applied Statistics  34 
Elective Courses
At least one of the elective courses should be a STATS 200level course. The remaining two elective courses may also be 200level courses. Alternatively, one or two elective courses may be approved courses in other departments. Special topics courses and seminars for undergraduates are offered from time to time by the department, and these may be counted toward the course requirement. Students may not count any Statistics courses below the 100 level toward the minor.
Examples of elective course sequences are:
Units  

Data Analysis and Applied Statistics  
STATS 202  Data Mining and Analysis  3 
STATS 203  Introduction to Regression Models and Analysis of Variance  3 
Statistical Methodology  
STATS 205  Introduction to Nonparametric Statistics  3 
STATS 206  Applied Multivariate Analysis  3 
STATS 207  Introduction to Time Series Analysis  3 
Economic Optimization  
STATS 206  Applied Multivariate Analysis  3 
ECON 160  Game Theory and Economic Applications  5 
Psychology Modeling and Experiments  
STATS 206  Applied Multivariate Analysis  3 
Signal Processing  
STATS 207  Introduction to Time Series Analysis  3 
EE 264  Digital Signal Processing  3 
EE 279  Introduction to Digital Communication  3 
Genetic and Ecologic Modeling  
STATS 217  Introduction to Stochastic Processes I  3 
BIO 283  Theoretical Population Genetics  3 
Probability and Applications  
STATS 217  Introduction to Stochastic Processes I  3 
STATS 218  Introduction to Stochastic Processes II  3 
Mathematical Finances  
STATS 240  Statistical Methods in Finance  34 
STATS 243  Risk Analytics and Management in Finance and Insurance  3 
STATS 250  Mathematical Finance  3 
Master of Science in Statistics
The University’s basic requirements for the M.S. degree are discussed in the “Graduate Degrees” section of this bulletin. The following are specific departmental requirements.
The M.S. in Statistics and the M.S. in Statistics, Data Science track, are intended as terminal degree programs and do not lead to the Ph.D. program in Statistics. Students interested in pursuing doctoral study in Statistics should apply directly to the Ph.D. program.
Admission
Prospective applicants should consult the Graduate Admissions and the Statistics Department admissions web pages for complete information on admission requirements and deadlines.
Recommended preparatory courses include advanced undergraduate level courses in linear algebra, statistics/probability and proficiency in programming.
Stanford students interested in the Data Science subplan in Statistics must apply as external candidates. Visit Graduate Admissions to start an application.
Coterminal Master's Program
Stanford undergraduates who want to apply for the coterminal master's degree must submit a complete application to the department by the deadline published on Statistics Department admissions web page.
Applications are accepted twice a year in Autumn and Winter quarters for the coterminal master's degree program in Statistics.
Students pursuing the Statistics coterminal master's degree must follow the same curriculum requirements stated in the Requirements for the Master of Science in Statistics section.
University Coterminal Requirements
Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this bulletin. University requirements for the coterminal master’s degree are described in the “Coterminal Master’s Program” section. University requirements for the master’s degree are described in the "Graduate Degrees" section of this bulletin.
After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. Transfer of courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case by case basis.
In this master’s program, courses taken three quarters prior to the first graduate quarter, or later, are eligible for consideration for transfer to the graduate career. No courses taken prior to the first quarter of the sophomore year may be used to meet master’s degree requirements.
Course transfers are not possible after the bachelor’s degree has been conferred.
The University requires that the graduate adviser be assigned in the student’s first graduate quarter even though the undergraduate career may still be open. The University also requires that the Master’s Degree Program Proposal be completed by the student and approved by the department by the end of the student’s first graduate quarter.
Master of Science in Statistics
Curriculum and Degree Requirements
The department requires that a master's student take 45 units of work from offerings in the Department of Statistics or from authorized courses in other departments. With the advice of the master's program advisers, each student selects his or her own set of electives.
Units for a given course may not be counted to meet the requirements of more than one degree, with the exception that up to 45 units of a Stanford M.A. or M.S. degree may be applied to the residency requirement for the Ph.D., D.M.A. or Engineer degrees. See the "Residency Policy for Graduate Students" section of this Bulletin for University rules.
As defined in the general graduate student requirements, students must maintain a grade point average (GPA) of 3.0 (or better) for courses used to fulfill degree requirements and classes must be taken at the 200 level or higher. No thesis is required.
Master's Degree Program Proposal
The Statistics Master's Degree Program Proposal form must be submitted to the department's student services administrator prior to the end of the first quarter of enrollment in the program.
A revised program proposal must be submitted if degree plans change.
There is no thesis requirement.
For further information about the Statistics master's degree program requirements, see the department web site.
1. Statistics Core Courses (must complete all four courses):
Units  

Probability  
STATS 116  Theory of Probability ^{1,2}  3 
Applied Statistics  
STATS 203  Introduction to Regression Models and Analysis of Variance  3 
or STATS 305A  Applied Statistics I  
or STATS 191  Introduction to Applied Statistics  
Theoretical Statistics  
STATS 200  Introduction to Statistical Inference  23 
or STATS 300A  Theory of Statistics I  
or STATS 370  A Course in Bayesian Statistics  
Stochastic Processes  
STATS 217  Introduction to Stochastic Processes I  23 
or STATS 218  Introduction to Stochastic Processes II  
or STATS 219  Stochastic Processes  
or STATS 318  Modern Markov Chains  
Students with prior background may replace each course with a more advanced course from the same area, or a more advanced course offered by the department, with consent of the adviser. All must be taken for a letter grade. 
2. Additional Statistics courses:
At least four additional Statistics courses must be taken from graduate offerings in the department (STATS 202 through 376A), all must be taken for a letter grade (with the exception of courses offered satisfactory/no credit only); except for the following courses that may only be used to fulfill elective credit^{ 3}: STATS 260A Workshop in Biostatistics series, STATS 299 Independent Study, STATS 298 Industrial Research for Statisticians, and STATS 390 Consulting Workshop.
3. Linear Algebra Mathematics Requirement:
Units  

Select one of the following:  
MATH 104  Applied Matrix Theory  3 
MATH 113  Linear Algebra and Matrix Theory  3 
MATH 115  Functions of a Real Variable  3 
MATH 171  Fundamental Concepts of Analysis  3 
Substitution of more advanced courses in Mathematics, that provide similar skills, may be made with consent of the adviser. All must be taken for a letter grade, with the exception of courses offered satisfactory/no credit only. 
4. Programming Requirement:
Units  

Select one of the following:  
CS 106A  Programming Methodology  3 
CS 106B  Programming Abstractions  3 
CS 106X  Programming Abstractions (Accelerated)  3 
CS 107  Computer Organization and Systems  35 
CME 108  Introduction to Scientific Computing  3 
Substitution more advanced courses in Computer Science, that provide similar skills, may be made with consent of the adviser. All must be taken for a letter grade, with the exception of courses offered satisfactory/no credit only. 
5. Elective Courses:
Additional elective units to complete the requirements may be chosen from the list available from the department web site. Other graduate courses (200 or above) may be authorized by the adviser if they provide skills relevant to degree requirements or deal primarily with an application of statistics or probability and do not overlap courses in the student's program.
There is sufficient flexibility to accommodate students with interests in applications to business, computing, economics, engineering, health, operations research, and biological and social sciences.
Students may enroll in up to 6 units of the following courses: ^{3}  
STATS 260A & STATS 260B & STATS 260C  Workshop in Biostatistics and Workshop in Biostatistics and Workshop in Biostatistics  12 
STATS 298  Industrial Research for Statisticians  1 
STATS 299  Independent Study  15 
STATS 390  Consulting Workshop  13 
Courses below 200 level are not acceptable, with the following exceptions; however, students are strongly advised to avoid redundancy in coursework:
Units  

STATS 191  Introduction to Applied Statistics  34 
MATH 115  Functions of a Real Variable  3 
MATH 171  Fundamental Concepts of Analysis  3 
CS 106A  Programming Methodology  35 
CS 106B  Programming Abstractions  35 
CS 106X  Programming Abstractions (Accelerated)  35 
CS 140  Operating Systems and Systems Programming  34 
CS 142  Web Applications  3 
CS 143  Compilers  34 
CS 144  Introduction to Computer Networking  34 
CS 145  Data Management and Data Systems  34 
CS 147  Introduction to HumanComputer Interaction Design  35 
CS 148  Introduction to Computer Graphics and Imaging  34 
CS 149  Parallel Computing  34 
CS 154  Introduction to Automata and Complexity Theory  34 
CS 155  Computer and Network Security  3 
CS 157  Computational Logic  3 
CS 161  Design and Analysis of Algorithms  35 
CS 170  Stanford Laptop Orchestra: Composition, Coding, and Performance  15 
CS 181  Computers, Ethics, and Public Policy ^{4}  4 
And at most, one of these courses ay be counted as an elective. ^{4}  
MATH 104  Applied Matrix Theory  3 
MATH 113  Linear Algebra and Matrix Theory  3 
STATS 116  Theory of Probability ^{2}  3 
^{1}  Students who replace STATS116 with STATS217 must take a second course in Stochastic Processes or Probability. 
^{2}  Enrollment in STATS 116 after successful completion of STATS 217, 218 and/or 219, cannot count it towards degree requirements, including as an elective. 
^{3}  Students admitted to the Statistics M.S. program prior to academic year 201819 fulfill the requirements in effect at the time of their admission. 
^{4}  Enrollment in a course that provides redundant coursework cannot be used to fulfill the M.S. degree requirements. 
Master of Science in Statistics, Data Science Track
The Data Science track develops strong mathematical, statistical, and computational and programming skills through the general master's core and programming requirements. In addition, it provides a fundamental data science education through general and focused electives requirement from courses in data sciences and related areas. Course choices are limited to predefined courses from the data sciences and related courses group. Programming requirement (requirement 4) is extended to 6 units and includes course work in advanced scientific programming and high performance computing. The final requirement is a practical component (requirement 5) for 6 units to be completed through capstone project, data science clinic, or other courses that have strong handson or practical component, such as statistical consulting.
Admission
Prospective applicants should consult the Graduate Admissions and the Statistics Department admissions webpages for complete information on admission requirements and deadlines.
Applicants apply to the Master of Science degree in Statistics and declare preference for the Data Science subplan (track) within the application ("Department Specialization" option).
Prerequisites
Recommended preparatory courses include advanced undergraduate level courses in linear algebra, probability, and introductory courses in PDEs, stochastics, numerical methods and proficiency in programming.
Curriculum and Degree Requirements
As defined in the general graduate student requirements, students must maintain a grade point average (GPA) of 3.0 or better and classes must be taken at the 200 level or higher. Students must complete 45 units of required coursework in Data Science.
A Master's Degree Program Proposal
This form must be signed and approved by the student's program adviser, is to be submitted by the student to the department's student services administrator prior to the end of the first quarter of enrollment in the program. A revised program proposal must be submitted if degree plans change.
No thesis is required.
The Data Science subplan (track) is printed on the transcript and diploma.
Requirement 1: Foundational (12 units)
Students must demonstrate foundational knowledge in the field by completing the following core courses. Courses in this area must be taken for letter grades.
Units  

CME 302  Numerical Linear Algebra  3 
CME 305  Discrete Mathematics and Algorithms  3 
CME 307  Optimization  3 
CME 308  Stochastic Methods in Engineering  3 
or CME 309  Randomized Algorithms and Probabilistic Analysis  
or STATS 310A  Theory of Probability I 
Requirement 2: Programming (6 units)
To ensure that students have a strong foundation in programming, 3 units of advanced scientific programming for letter grade at the level of CME 212 and three units of parallel computing. Courses in this area must be taken for letter grades.
Programming proficiency at the level of CME 211 is a hard prerequisite for CME 212 (students may only place out of 211 with prior written approval).
Units  

Advanced Scientific Programming: (3 units)  
CME 211  Software Development for Scientists and Engineers (can only be used as an elective)  3 
CME 212  Advanced Software Development for Scientists and Engineers  3 
Parallel Computing/HCP courses: (3 units)  
CME 213  Introduction to parallel computing using MPI, openMP, and CUDA  3 
CME 323  Distributed Algorithms and Optimization  3 
CME 342  Parallel Methods in Numerical Analysis  3 
CS 149  Parallel Computing  34 
CS 316  Advanced MultiCore Systems  3 
Requirement 3: Data Science Electives (12 units)
Data Science electives should demonstrate breadth of knowledge in the technical area. The elective course list is defined. Courses outside this list are subject to approval. Courses in this area must be taken for letter grades.
Units  

STATS 200  Introduction to Statistical Inference  3 
or STATS 300A  Theory of Statistics I  
STATS 203  Introduction to Regression Models and Analysis of Variance  3 
or STATS 305A  Applied Statistics I  
STATS 315A  Modern Applied Statistics: Learning  23 
STATS 315B  Modern Applied Statistics: Data Mining  23 
or equivalent courses as approved by the adviser. 
Requirement 4: Specialized Electives (9 units)
Choose three courses in specialized areas from the following list. Courses outside this list are subject to approval.
Units  

BIOE 214  Representations and Algorithms for Computational Molecular Biology  34 
BIOMEDIN 215  Data Driven Medicine  3 
BIOS 221/STATS 366  Modern Statistics for Modern Biology  3 
CS 224W  Analysis of Networks  34 
CS 229  Machine Learning  34 
CS 231N  Convolutional Neural Networks for Visual Recognition  3 
CS 246  Mining Massive Data Sets  34 
CS 448  Topics in Computer Graphics  34 
ECON 293  Machine Learning and Causal Inference  3 
ENERGY 240  Data science for geoscience  3 
OIT 367  Business Intelligence from Big Data  3 
PSYCH 204A  Human Neuroimaging Methods  3 
STATS 290  Computing for Data Science  3 
Requirement 5: Practical Component (6 units)
Students are required to take 6 units of practical component that may include any combination of:

Project labs offered by Stanford Data Lab: ENGR 150 Data Challenge Lab, and ENGR 350 Data Impact Lab. (Limited enrollment; application required.)

Master's research: STATS 299 Independent Study. A capstone project, supervised by a faculty member and approved by the student's adviser. The research project should be computational in nature. Students should submit a onepage proposal, supported by the faculty member and sent to the student's Data Science adviser for approval (at least one quarter prior to start of project). Should be taken for a letter grade.

Other courses that have a strong handson and practical component, such as STATS 390 Consulting Workshop .
Doctor of Philosophy in Statistics
The department looks for students who wish to prepare for research careers in statistics or probability, either applied or theoretical. Advanced undergraduate or master's level work in mathematics and statistics provides a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. The program normally takes five years to complete.
Program Summary
Units  

Firstyear core program  
STATS 300  Advanced Topics in Statistics (offered Summer Quarter)  23 
STATS 300A  Theory of Statistics I  23 
STATS 300B  Theory of Statistics II  24 
STATS 300C  Theory of Statistics III  24 
STATS 305A  Applied Statistics I  23 
STATS 305B  Applied Statistics II  23 
STATS 305C  Applied Statistics III  23 
STATS 310A  Theory of Probability I  24 
STATS 310B  Theory of Probability II  23 
STATS 310C  Theory of Probability III  24 
 Pass two of three parts of the qualifying examinations (end of first year); breadth requirement (second, third and fourth year); successfully complete the dissertation proposal meeting (early spring quarter of third year); pass the University oral examination (fourth or fifth year); dissertation (fifth year).
 In addition, students are required to take nine units of advanced topics courses offered by the department. Recommended courses include the following:
Units  

STATS 314A  Advanced Statistical Theory  3 
STATS 315A  Modern Applied Statistics: Learning  23 
STATS 315B  Modern Applied Statistics: Data Mining  23 
STATS 317  Stochastic Processes  3 
STATS 318  Modern Markov Chains  3 
STATS 330  An Introduction to Compressed Sensing  3 
STATS 370  A Course in Bayesian Statistics  3 
STATS 376A  Information Theory  3 
STATS 376B  Topics in Information Theory and Its Applications  3 
EE 364A  Convex Optimization I  3 
 Complete a minimum of three units of STATS 390 Consulting Workshop, taking it at least twice.
 Take STATS 319 Literature of Statistics once per year after passing the Qualifying Exam until the year after passing the dissertation proposal meeting.
FirstYear Core Courses
 STATS 300A Theory of Statistics I, STATS 300B Theory of Statistics II and STATS 300C Theory of Statistics III systematically survey the ideas of estimation and of hypothesis testing for parametric and nonparametric models involving small and large samples.
 STATS 305A Applied Statistics I is concerned with linear regression and the analysis of variance.
 STATS 305B Applied Statistics II and STATS 305C Applied Statistics III survey a large number of modeling techniques, related to but going beyond the linear models of STATS 305A Applied Statistics I.
 STATS 310A Theory of Probability I, STATS 310B Theory of Probability II, and STATS 310C Theory of Probability III are measuretheoretic courses in probability theory, beginning with basic concepts of the law of large numbers and martingale theory.
Students who do not have enough mathematics background can take STATS 310A,B,C after their first year but need to have their firstyear program approved by the Director of Graduate Studies.
Qualifying Examinations
These are intended to test the student's level of knowledge when the firstyear program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, and all are typically taken during the summer between the student's first and second years. Students are expected to show acceptable performance in two examinations. Letter grades are not given. After passing the qualifying exams students file for Ph.D. candidacy, a University milestone.
Breadth Requirement
Students are required to take 15 units of coursework outside of the department and are advised to choose an area of concentration in a specific scientific field of statistical applications approved by their Ph.D. program adviser.
Popular areas include: Computational Biology and Statistical Genomics, Machine Learning, Applied Probability, Earth Science Statistics, and Social and Behavioral Sciences.
Dissertation Reading Committee, Dissertation Proposal Meeting and University Oral Examinations
The dissertation reading committee consists of the student's adviser plus two faculty readers, all of whom are responsible for reading and approving the full dissertation.
The dissertation proposal meeting is intended to demonstrate students' depth in some areas of statistics, and to examine the general plan for their research. It also confirms that students have chosen a Ph.D. faculty adviser and have started to work with that adviser on a research topic. In the meeting, the student will give a 50minute presentation and discuss their ideas for completing a Ph.D. thesis, with a committee typically consisting of the members of the dissertation reading committee. The meeting must be successfully completed by early spring quarter of the third year. "Successful completion" means that the general research plan is sound and has a reasonable chance of success. If the student does not pass, the meeting must be repeated. Repeated failure by the end of Year 3 can lead to a loss of financial support.
The oral examination/dissertation defense is scheduled when the student has finished their dissertation and is in the process of completing their final draft. The oral exam consists of a 50minute presentation on the dissertation topic, followed by a question and answer period attended only by members of the examining committee. The questions relate both to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed within the last few months of the student's Ph.D. period. The examining committee usually consists of at least five members: four examiners including the three members of the Dissertation Reading Committee, plus an outside chair who serves as an impartial representative of the academic standards of the University. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the written dissertation.
For further information on University oral examinations and committees, see the Graduate Academic Policies and Procedures (GAP) Handbook, section 4.7 or the "University Oral Examination" section of this bulletin.
Doctoral and Research Advisers
From the student's arrival until the selection of a research adviser, the student's academic progress is monitored by the department's Director of Graduate Studies. Each student should meet at least once a quarter with the Doctoral Adviser to discuss their academic plans and their progress towards choosing a dissertation adviser.
Financial Support
Students accepted to the Ph.D. program are offered financial support. All tuition expenses are paid and there is a fixed monthly stipend determined to be sufficient to pay living expenses. Financial support can be continued for five years, department resources permitting, for students in good standing. The resources for student financial support derive from funds made available for student teaching and research assistantships. Students receive both a teaching and research assignment each quarter which, together, do not exceed 20 hours. Students are encouraged to apply for outside scholarships, fellowships, and other forms of financial support.
Ph.D. Minor in Statistics
Students must complete 30 total units for the Ph.D. minor. 20 units must be from Statistics courses numbered 300 and above and taken for a letter grade (minimum grade of B for each course). The remaining 10 units can be from Statistics courses numbered 200 and above, and may be taken for a letter grade (minimum grade of B for each course) or credit. Students may not include more than three units of Stats 390, Consulting Workshop, towards the 30 units. The selection of courses must be approved by the Director of Graduate Studies. The Application for the Ph.D. Minor form must be approved by both the student's Ph.D. department and the Statistics department.
For further information about the Statistics Ph.D. degree program requirements, see the department web site.
Graduate Advising Expectations
The Department of Statistics is committed to providing academic advising in support of graduate student scholarly and professional development. When most effective, this advising relationship entails collaborative and sustained engagement by both the adviser and the advisee. As a best practice, advising expectations should be periodically discussed and reviewed to ensure mutual understanding. Both the adviser and the advisee are expected to maintain professionalism and integrity.
Faculty advisers guide students in key areas such as selecting courses, designing and conducting research, developing of teaching pedagogy, navigating policies and degree requirements, and exploring academic opportunities and professional pathways.
Graduate students are active contributors to the advising relationship, proactively seeking academic and professional guidance and taking responsibility for informing themselves of policies and degree requirements for their graduate program.
For a statement of University policy on graduate advising, see the "Graduate Advising" section of this bulletin.
Faculty
Emeriti: (Professors) Jerome H. Friedman, Paul Switzer
Chair: Art Owen
Professors: Emmanuel Candès, Sourav Chatterjee, Amir Dembo, Persi Diaconis, David L. Donoho, Bradley Efron, Trevor J. Hastie, Susan P. Holmes, Iain M. Johnstone, Tze L. Lai, Andrea Montanari, Art Owen, Joseph P. Romano, Chiara Sabatti, David O. Siegmund, Jonathan Taylor, Robert J. Tibshirani, Guenther Walther, Wing H. Wong
Assistant Professors: John Duchi, Tengyu Ma, Julia Palacios
Courtesy Professors: John Ioannidis, Hua Tang
Courtesy Associate Professors: David Rogosa, Lu Tian
Courtesy Assistant Professors: Mike Baiocchi, Percy Shuo Liang, Stefan Wager
Stein Fellows: James Johndrow, Yuting Wei, Lucy Xia, Yumeng Zhang, Xiang Zhu
Courses
STATS 32. Introduction to R for Undergraduates. 1 Unit.
This short course runs for weeks two through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to nonengineering students. Laptops necessary for use in class.
STATS 42Q. Undergraduate Admissions to Selective Universities  a Statistical Perspective. 2 Units.
The goal is the building of a statistical model, based on applicant data, for predicting admission to selective universities. The model will consider factors such as gender, ethnicity, legacy status, publicprivate schooling, test scores, effects of early action, and athletics. Common misconceptions and statistical pitfalls are investigated. The applicant data are not those associated with any specific university.
STATS 48N. Riding the Data Wave. 3 Units.
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
STATS 50. Mathematics of Sports. 3 Units.
This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal ingame strategies and ensure fairness between participants. Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis. The course is intended to focus on databased applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e.g, STATS 60,101 or 116, are recommended. A prior knowledge of Linear Algebra (e.g., MATH 51) and basic probability is strongly recommended.
Same as: MCS 100
STATS 60. Introduction to Statistical Methods: Precalculus. 5 Units.
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Same as: PSYCH 10, STATS 160
STATS 101. Data Science 101. 5 Units.
http://web.stanford.edu/class/stats101/ . This course will provide a handson introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore a core topic comprising three lectures and two labs (a module), in which students will manipulate realworld data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software (Jupyter, R). The objectives of this course are to have students (1) be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and (2) be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures. No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend. Open to graduates as well.
STATS 110. Statistical Methods in Engineering and the Physical Sciences. 45 Units.
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
STATS 116. Theory of Probability. 35 Units.
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent.
STATS 141. Biostatistics. 35 Units.
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Same as: BIO 141
STATS 155. Statistical Methods in Computational Genetics. 3 Units.
The computational methods necessary for the construction and evaluation of sequence alignments and phylogenies built from molecular data and genetic data such as microarrays and data base searches. How to formulate biological problems in an algorithmic decomposed form, and building blocks common to many problems such as Markovian models, multivariate analyses. Some software covered in labs (Python, Biopython, XGobi, MrBayes, HMMER, Probe). Prerequisites: knowledge of probability equivalent to STATS 116, STATS 202 and one class in computing at the CS 106 level. Writing intensive course for undergraduates only. Instructor consent required. (WIM).
STATS 160. Introduction to Statistical Methods: Precalculus. 5 Units.
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Same as: PSYCH 10, STATS 60
STATS 167. Probability: Ten Great Ideas About Chance. 4 Units.
Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Same as: PHIL 166, PHIL 266, STATS 267
STATS 191. Introduction to Applied Statistics. 34 Units.
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and crossvalidation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisite: introductory statistical methods course. Recommended: 60, 110, or 141.
STATS 195. Introduction to R. 1 Unit.
This short course runs for four weeks and is offered in fall and spring. It is recommended for students who want to use R in statistics, science or engineering courses, and for students who want to learn the basics of data science with R. The goal of the short course is to familiarize students with some of the most important R tools for data analysis. Lectures will focus on learning by example and assignments will be applicationdriven. No prior programming experience is assumed.
Same as: CME 195
STATS 196A. Multilevel Modeling Using R. 1 Unit.
See http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: twolevel nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, threelevel analyses. Class meets April 10, April 17, April 24, May 1, May 15.
Same as: EDUC 401D
STATS 199. Independent Study. 115 Unit.
For undergraduates.
STATS 200. Introduction to Statistical Inference. 3 Units.
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; NeymanPearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116. http://statweb.stanford.edu/~owen/courses/200.
STATS 202. Data Mining and Analysis. 3 Units.
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g., STATS 60), linear algebra (e.g., MATH 51), and computer programming (e.g., CS 105).
STATS 203. Introduction to Regression Models and Analysis of Variance. 3 Units.
Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre or corequisite: 200.
STATS 203V. Introduction to Regression Models and Analysis of Variance. 3 Units.
Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. This course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Google Hangout or BlueJeans. Pre/Corequisite: 200 or equivalent.
STATS 204. Sampling. 3 Units.
How best to take data and where to sample it. Examples include surveys and sampling from data warehouses. Emphasis is on methods for finite populations. Topics: simple random sampling, stratified sampling, cluster sampling, ratio and regression estimators, two stage sampling.
STATS 205. Introduction to Nonparametric Statistics. 3 Units.
Nonparametric analogs of the one and twosample ttests and analysis of variance; the sign test, median test, Wilcoxon's tests, and the KruskalWallis and Friedman tests, tests of independence. Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates.
STATS 206. Applied Multivariate Analysis. 3 Units.
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computerintensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre or corequisite: 200.
STATS 207. Introduction to Time Series Analysis. 3 Units.
Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and statespace models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
STATS 208. Introduction to the Bootstrap. 3 Units.
The bootstrap is a computerbased method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and confidence intervals; related resampling methods including the jackknife, crossvalidation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability.
STATS 209. Statistical Methods for Group Comparisons and Causal Inference. 3 Units.
See http://rogosateaching.com/stat209/. Critical examination of statistical methods in social science and life sciences applications, especially for cause and effect determinations. Topics: mediating and moderating variables, potential outcomes framework, encouragement designs, multilevel models, heterogeneous treatment effects, matching and propensity score methods, analysis of covariance, instrumental variables, compliance, path analysis and graphical models, group comparisons with longitudinal data. Prerequisite: intermediatelevel statistical methods.
Same as: EDUC 260A, HRP 239
STATS 211. Metaresearch: Appraising Research Findings, Bias, and Metaanalysis. 3 Units.
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Metaanalysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a metaresearch project or reworking and evaluation of an existing published metaanalysis. Prerequisite: knowledge of basic statistics.
Same as: CHPR 206, HRP 206, MED 206
STATS 212. Applied Statistics with SAS. 3 Units.
Data analysis and implementation of statistical tools in SAS. Topics: reading in and describing data, categorical data, dates and longitudinal data, correlation and regression, nonparametric comparisons, ANOVA, multiple regression, multivariate data analysis, using arrays and macros in SAS. Prerequisite: statistical techniques at the level of STATS 191 or 203; knowledge of SAS not required.
STATS 213. Introduction to Graphical Models. 3 Units.
Multivariate Normal Distribution and Inference, Wishart distributions, graph theory, probabilistic Markov models, pairwise and global Markov property, decomposable graph, Markov equivalence, MLE for DAG models and undirected graphical models, Bayesian inference for DAG models and undirected graphical models. Prerequisites: STATS 217, STATS 200 (preferably STATS 300A), MATH 104 or equivalent class in linear algebra.
Same as: STATS 313
STATS 215. Statistical Models in Biology. 3 Units.
Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the WrightFisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as in STATS 217, but emphasis is on examples drawn from applications in biology, especially genetics. Prerequisite: 116 or equivalent.
STATS 216. Introduction to Statistical Learning. 3 Units.
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered via video segments (MOOC style), and inclass problem solving sessions. Prereqs: Introductory courses in statistics or probability (e.g., STATS 60 or STATS 101), linear algebra (e.g., MATH 51), and computer programming (e.g., CS 105).
STATS 216V. Introduction to Statistical Learning. 3 Units.
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Google Hangout or BlueJeans. There are four homework assignments, a midterm, and a final exam, all of which are administered remotely. Prereqs: Introductory courses in statistics or probability (e.g., STATS 60 or STATS 101), linear algebra (e.g., MATH 51), and computer programming (e.g., CS 105).
STATS 217. Introduction to Stochastic Processes I. 23 Units.
Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. NonStatistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of instructor.
STATS 218. Introduction to Stochastic Processes II. 3 Units.
Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.
STATS 219. Stochastic Processes. 3 Units.
Introduction to measure theory, Lp spaces and Hilbert spaces. Random variables, expectation, conditional expectation, conditional distribution. Uniform integrability, almost sure and Lp convergence. Stochastic processes: definition, stationarity, sample path continuity. Examples: random walk, Markov chains, Gaussian processes, Poisson processes, Martingales. Construction and basic properties of Brownian motion. Prerequisite: STATS 116 or MATH 151 or equivalent. Recommended: MATH 115 or equivalent. http://statweb.stanford.edu/~adembo/math136/.
Same as: MATH 136
STATS 222. Statistical Methods for Longitudinal Research. 23 Units.
See http://rogosateaching.com/stat222/. Research designs and statistical procedures for timeordered (repeatedmeasures) data. The analysis of longitudinal panel data is central to empirical research on learning, development, aging, and the effects of interventions. Topics include: measurement of change, growth curve models, analysis of durations including survival analysis, experimental and nonexperimental group comparisons, reciprocal effects, stability. Prerequisite: intermediate statistical methods.
Same as: EDUC 351A
STATS 229. Machine Learning. 34 Units.
Topics: statistical pattern recognition, linear and nonlinear regression, nonparametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.
Same as: CS 229
STATS 231. Statistical Learning Theory. 3 Units.
How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. We will present various learning algorithms and prove theoretical guarantees about them. Topics include generalization bounds, implicit regularization, the theory of deep learning, spectral methods, and online learning and bandits problems. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (STATS 315A or CS 229).
Same as: CS 229T
STATS 237. Risk Management, Insurance, Portfolios and Derivative Securities. 24 Units.
Asset returns and their volatilities. Markowitz portfolio theory, capital asset pricing model, multifactor pricing models. Measures of market risk. Financial derivatives and hedging. BlackScholes pricing of European options. Valuation of American options. Implied volatility and the Greeks. Prerequisite: STATS 116 or equivalent.
STATS 237P. Risk Management, Insurance, Portfolios and Derivative Securities. 3 Units.
For SCPD students; see STATS237.
STATS 238. The Future of Finance. 2 Units.
(Same as Law 1038) If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved postfinancial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fastgrowing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guestlecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a nontechnical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and email to the instructors the Consent Application Form, which is available on the Public Policy Program's website at https://publicpolicy.stanford.edu/academics/undergraduate/forms. See Consent Application Form for submission deadline.
Same as: ECON 152, ECON 252, PUBLPOL 364
STATS 239. Mathematical and Computational Finance Seminar. 1 Unit.
May be repeat for credit.
Same as: CME 242, MS&E 446A
STATS 240. Statistical Methods in Finance. 24 Units.
(SCPD students register for 240P.) Regression analysis and applications to investment models. Principal components and multivariate analysis. Likelihood inference and Bayesian methods. Financial time series. Estimation and modeling of volatilities. Statistical methods for portfolio management. Prerequisite: STATS 200 or equivalent.
STATS 240P. Statistical Methods in Finance. 3 Units.
For SCPD students; see 240.
STATS 241. Datadriven Financial Econometrics. 24 Units.
(SCPD students register for 241P) Substantive and empirical modeling approaches in options, interest rate, and credit markets. Nonlinear least squares, logistic regression and generalized linear models. Nonparametric regression and model selection. Multivariate time series modeling and forecasting. Vector autoregressive models and cointegration. Risk measures, models and analytics. Prerequisite or corequisite: STATS 240 or equivalent.
STATS 241P. Datadriven Financial Econometrics. 3 Units.
For SCPD students; see STATS241.
STATS 243. Risk Analytics and Management in Finance and Insurance. 24 Units.
Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite: STATS 240 or equivalent.
Same as: CME 243
STATS 243P. Risk Analytics and Management in Finance and Insurance. 3 Units.
For SCPD students; see STATS243.
STATS 244. Quantitative Trading: Algorithms, Data, and Optimization. 24 Units.
Statistical trading rules and performances evaluation. Active portfolio management and dynamic investment strategies. Data analytics and models of transactions data. Limit order book dynamics in electronic exchanges. Algorithmic trading, informatics, and optimal execution. Market making and inventory control. Risk management and regulatory issues. Prerequisites: STATS 240 or equivalent.
STATS 244P. Quantitative Trading: Algorithms, Data and Optimization. 3 Units.
For SCPD students; see 244.
STATS 245. Data, Models and Applications to Healthcare Analytics. 24 Units.
Topics on fundamentals of data science, biological and statistical models, application to medical product safety evaluation, health risk models and their evaluation, benefitrisk assessment and multicriteria decision analytics. Applications to environmental health, nutritional epidemiology, wellness and prevention will also be discussed. Prerequisite: Graduate students  STATS 202 or 216, or CS 229; Undergraduate students  consent of instructor.
STATS 245P. Data, Models, and Applications to Healthcare Analytics. 3 Units.
For SCPD students; see STATS245.
STATS 246. Deep Learning and AI Applications to Medical Imaging and Healthcare. 24 Units.
tbd.
STATS 246P. Deep Learning and AI Applications to Medical Imaging and Healthcare. 3 Units.
For SCPD students: others see STATS 246.
STATS 247. Mobile Health and Personalized Recommendation Technology. 24 Units.
(SCPD students register for 247P). Description TBD.
STATS 247P. Mobile Health and Personalized Recommendation Technology. 3 Units.
For SCPD students; others see STATS247.
STATS 248. Clinical Trial Design in the Age of Precision Medicine and Health. 24 Units.
Traditional designs for Phase I, II, and III clinical trials for medical product approval and Phase IV postmarketing studies for safety evaluation cost too much and take too much time in the era of precision medicine and precision health. This course introduces innovative designs that have been developed for affordable clinical trials, which can be completed within reasonable time constraints and which have been encouraged by regulatory agencies. Prerequisities STATS 200 or equivalent; working knowledge of clinical trials.
Same as: BIODS 248
STATS 248P. Clinical Trial Design in the Age of Precision Medicine and Health. 3 Units.
For SCPD students; others see STATS 248.
STATS 250. Mathematical Finance. 3 Units.
Stochastic models of financial markets. Forward and futures contracts. European options and equivalent martingale measures. Hedging strategies and management of risk. Term structure models and interest rate derivatives. Optimal stopping and American options. Corequisites: MATH 236 and 227 or equivalent.
Same as: MATH 238
STATS 253. Analysis of Spatial and Temporal Data. 3 Units.
A unified treatment of methods for spatial data, time series, and other correlated data from the perspective of regression with correlated errors. Two main paradigms for dealing with autocorrelation: covariance modeling (kriging) and autoregressive processes. Bayesian methods. Prerequisites: applied linear algebra (MATH 103 or equivalent), statistical estimation (STATS 200 or CS 229), and linear regression (STATS 203 or equivalent).
STATS 260A. Workshop in Biostatistics. 12 Unit.
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Same as: BIODS 260A
STATS 260B. Workshop in Biostatistics. 12 Unit.
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Same as: BIODS 260B
STATS 260C. Workshop in Biostatistics. 12 Unit.
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Same as: BIODS 260C
STATS 261. Intermediate Biostatistics: Analysis of Discrete Data. 3 Units.
Methods for analyzing data from casecontrol and crosssectional studies: the 2x2 table, chisquare test, Fisher's exact test, odds ratios, MantelHaenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: crossfold validation and bootstrap inference.
Same as: BIOMEDIN 233, HRP 261
STATS 262. Intermediate Biostatistics: Regression, Prediction, Survival Analysis. 3 Units.
Methods for analyzing longitudinal data. Topics include KaplanMeier methods, Cox regression, hazard ratios, timedependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeatedmeasures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.
Same as: HRP 262
STATS 263. Design of Experiments. 3 Units.
Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. BoxBehnken. Taguchi methods. Computer experiments and space filling designs. Prerequisites: probability at STATS 116 level or higher, and at least one course in linear models.
Same as: STATS 363
STATS 266. Advanced Statistical Methods for Observational Studies. 23 Units.
Design principles and statistical methods for observational studies. Topics include: matching methods, sensitivity analysis, and instrumental variables. 3 unit registration requires a small project and presentation. Computing is in R. Prerequisites: HRP 261 and 262 or STATS 209 (HRP 239), or equivalent. See http://rogosateaching.com/somgen290/.
Same as: CHPR 266, EDUC 260B, HRP 292
STATS 267. Probability: Ten Great Ideas About Chance. 4 Units.
Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Same as: PHIL 166, PHIL 266, STATS 167
STATS 270. A Course in Bayesian Statistics. 3 Units.
This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference.
Same as: STATS 370
STATS 271. Applied Bayesian Statistics. 3 Units.
This course is a modern treatment of applied Bayesian statistics with a focus on highdimensional problems. We will study a collection of canonical methods that see heavy use in applications, including highdimensional linear and generalized linear models, hierarchical/random effects models, Gaussian processes, variabledimension and Dirichlet process mixtures, graphical models, and methods used in Bayesian inverse problems. Each method will be accompanied by one or more motivating datasets. Through these examples the course will cover: (1) Bayesian hypothesis testing, multiplicity correction, selection, shrinkage, and model averaging; (2) prior choice; (3) Frequentist properties of Bayesian procedures in high dimensions; and (4) computation by Markov chain Monte Carlo, including constructing efficient Gibbs, Metropolis, and more exotic samplers, empirical convergence analysis, strategies for scaling computation to high dimensions (approximations, divideandconquer, minibatching, et cetera), and the theory of convergence rates.
Same as: STATS 371
STATS 285. Massive Computational Experiments, Painlessly. 12 Unit.
Ambitious Data Science requires massive computational experimentation; the entry ticket for a solid PhD in some fields is now to conduct experiments involving 1 Million CPU hours. Recently several groups have created efficient computational environments that make it painless to run such massive experiments. This course reviews stateoftheart practices for doing massive computational experiments on compute clusters in a painless and reproducible manner. Students will learn how to automate their computing experiments first of all using nutsandbolts tools such as Perl and Bash, and later using available comprehensive frameworks such as ClusterJob and CodaLab, which enables them to take on ambitious Data Science projects. The course also features few guest lectures by renowned scientists in the field of Data Science. Students should have a familiarity with computational experiments and be facile in some highlevel computer language such as R, Matlab, or Python.
STATS 290. Computing for Data Science. 3 Units.
Programming and computing techniques for the requirements of data science: acquisition and organization of data; visualization, modelling and inference for scientific applications; presentation and interactive communication of results. Emphasis on computing for substantial projects. Software development with emphasis on R, plus other key software tools. Prerequisites: Programming experience including familiarity with R; computing at least at the level of CS 106; statistics at the level of STATS 110 or 141.
STATS 298. Industrial Research for Statisticians. 1 Unit.
Masterslevel research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an offcampus employer. Students must submit a written final report upon completion of the internship in order to receive credit. Repeatable for credit. Prerequisite: enrollment in Statistics M.S. program.
STATS 299. Independent Study. 15 Unit.
For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.
STATS 300. Advanced Topics in Statistics. 23 Units.
TBD.
STATS 300A. Theory of Statistics I. 23 Units.
Finite sample optimality of statistical procedures; Decision theory: loss, risk, admissibility; Principles of data reduction: sufficiency, ancillarity, completeness; Statistical models: exponential families, group families, nonparametric families; Point estimation: optimal unbiased and equivariant estimation, Bayes estimation, minimax estimation; Hypothesis testing and confidence intervals: uniformly most powerful tests, uniformly most accurate confidence intervals, optimal unbiased and invariant tests. Prerequisites: Real analysis, introductory probability (at the level of STATS 116), and introductory statistics.
STATS 300B. Theory of Statistics II. 24 Units.
Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: NeymanPearson theory; UMP tests and uniformly most accurate confidence intervals; use of unbiasedness and invariance to eliminate nuisance parameters. Large sample theory: basic convergence concepts; robustness; efficiency; contiguity, locally asymptotically normal experiments; convolution theorem; asymptotically UMP and maximin tests. Asymptotic theory of likelihood ratio and score tests. Rank permutation and randomization tests; jackknife, bootstrap, subsampling and other resampling methods. Further topics: sequential analysis, optimal experimental design, empirical processes with applications to statistics, Edgeworth expansions, density estimation, time series.
STATS 300C. Theory of Statistics III. 24 Units.
Decision theory formulation of statistical problems. Minimax, admissible procedures. Complete class theorems ("all" minimax or admissible procedures are "Bayes"), Bayes procedures, conjugate priors, hierarchical models. Bayesian non parametrics: diaichlet, tail free, polya trees, bayesian sieves. Inconsistency of bayes rules.
STATS 302. Qualifying Exams Workshop. 3 Units.
Prepares Statistics Ph.D. students for the qualifying exams by reviewing relevant course topics and problem solving strategies.
STATS 303. PhD First Year Student Workshop. 1 Unit.
For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.
STATS 305A. Applied Statistics I. 23 Units.
Statistics of real valued responses. Review of multivariate normal distribution theory. Univariate regression. Multiple regression. Constructing features from predictors. Geometry and algebra of least squares: subspaces, projections, normal equations, orthogonality, rank deficiency, GaussMarkov. GramSchmidt, the QR decomposition and the SVD. Interpreting coefficients. Collinearity. Dependence and heteroscedasticity. Fits and the hat matrix. Model diagnostics. Model selection, Cp/AIC and crossvalidation, stepwise, lasso. Multiple comparisons. ANOVA, fixed and random effects. Use of bootstrap and permutations. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A and 306B).
STATS 305B. Applied Statistics II. 23 Units.
Methods for discrete responses. Logistic regression. Poisson loglinear models. Probit model. Generalized linear models. Exponential families. Overdispersion. Tests of independence. Cox proportional hazards model. BradleyTerry models. CochranMantelHaenszel. Matched pairs. Generalized linear mixed models. Problem sets involve working with real data sets. Additional topics selected by the instructor. Prerequisites: 305A or consent of the instructor. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A and 306B).
STATS 305C. Applied Statistics III. 23 Units.
Methods for multivariate responses. Theory, computation and practice for multivariate statistical tools. Multivariate Gaussian and undirected graphical models, graphical displays. Hotelling¿s Tsquared, principal components, canonical correlations, linear discriminant analysis, correspondence analysis, and recent variants of these. Hierarchical and kmeans clustering. Biclustering. Factor analysis and independent component analysis. Topic modeling. Multidimensional scaling and variants (e.g., Isomap, spectral clustering, tSNE). Matrix completion. Extensive work with data involving programming ¿ ideally in R. nPrerequisites: STATS 305A and STATS 305B or consent of the instructor. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A and 306B).
STATS 310A. Theory of Probability I. 24 Units.
Mathematical tools: sigma algebras, measure theory, connections between coin tossing and Lebesgue measure, basic convergence theorems. Probability: independence, BorelCantelli lemmas, almost sure and Lp convergence, weak and strong laws of large numbers. Large deviations. Weak convergence; central limit theorems; Poisson convergence; Stein's method. Prerequisites: STATS 116, MATH 171.
Same as: MATH 230A
STATS 310B. Theory of Probability II. 23 Units.
Conditional expectations, discrete time martingales, stopping times, uniform integrability, applications to 01 laws, RadonNikodym Theorem, ruin problems, etc. Other topics as time allows selected from (i) local limit theorems, (ii) renewal theory, (iii) discrete time Markov chains, (iv) random walk theory,n(v) ergodic theory. Prerequisite: 310A or MATH 230A.
Same as: MATH 230B
STATS 310C. Theory of Probability III. 24 Units.
Continuous time stochastic processes: martingales, Brownian motion, stationary independent increments, Markov jump processes and Gaussian processes. Invariance principle, random walks, LIL and functional CLT. Markov and strong Markov property. Infinitely divisible laws. Some ergodic theory. Prerequisite: 310B or MATH 230B. http://statweb.stanford.edu/~adembo/stat310c/.
Same as: MATH 230C
STATS 311. Information Theory and Statistics. 3 Units.
Information theoretic techniques in probability and statistics. Fano, Assouad,nand Le Cam methods for optimality guarantees in estimation. Large deviationsnand concentration inequalities (Sanov's theorem, hypothesis testing, thenentropy method, concentration of measure). Approximation of (Bayes) optimalnprocedures, surrogate risks, fdivergences. Penalized estimators and minimumndescription length. Online game playing, gambling, noregret learning. Prerequisites: EE 376A (or equivalent) or STATS 300A.
Same as: EE 377
STATS 312. Statistical Methods in Neuroscience. 3 Units.
The goal is to discuss statistical methods for neuroscience in their natural habitat: the research questions, measurement technologies and experiment designs used in modern neuroscience. We will emphasize both the choice and quality of the methods, as well as the reporting, interpretation and visualization of results. Likely topics include preprocessing and signal extraction for singleneuron and neuroimaging technologies, statistical models for single response, encoding and decoding models, multipleresponses and parametric maps, and testing. Participation includes analyzing methods and real data, discussing papers in class, and a final project. Requirements: we will assume familiarity with linear models, likelihoods etc. Students who have not taken graduate level statistics courses are required to contact the instructor. Background in neuroscience is not assumed.
STATS 313. Introduction to Graphical Models. 3 Units.
Multivariate Normal Distribution and Inference, Wishart distributions, graph theory, probabilistic Markov models, pairwise and global Markov property, decomposable graph, Markov equivalence, MLE for DAG models and undirected graphical models, Bayesian inference for DAG models and undirected graphical models. Prerequisites: STATS 217, STATS 200 (preferably STATS 300A), MATH 104 or equivalent class in linear algebra.
Same as: STATS 213
STATS 314A. Advanced Statistical Theory. 3 Units.
Covers a range of topics, including: empirical processes, asymptotic efficiency, uniform convergence of measures, contiguity, resampling methods, Edgeworth expansions.
STATS 314B. Topics in Minimax Inference of Nonparametric Functionals. 3 Units.
Topics in the estimation of various functionals of underlying distribution for nonparametric problems. Development of ideas of higher order influence functions that extend the theory of classical first order semiparametric theory. Topics on adaptive estimation and adaptive confidence sets construction. Understanding results from wavelet theory and higher order Ustatistics.
STATS 315A. Modern Applied Statistics: Learning. 23 Units.
Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization. Kernel methods. Generalized additive models. Kernel smoothing. Gaussian mixtures and the EM algorithm. Model assessment and selection: crossvalidation and the bootstrap. Pathwise coordinate descent. Sparse graphical models. Prerequisites: STATS 305, 306A,B or consent of instructor.
STATS 315B. Modern Applied Statistics: Data Mining. 23 Units.
Twopart sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes, in particular, regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a predictive goal, viewed from a statistical perspective as computer automated exploratory analysis of large complex data sets.
STATS 316. Stochastic Processes on Graphs. 13 Unit.
Local weak convergence, Gibbs measures on trees, cavity method, and replica symmetry breaking. Examples include random ksatisfiability, the assignment problem, spin glasses, and neural networks. Prerequisite: 310A or equivalent. https://web.stanford.edu/~montanar/TEACHING/Stat316/stat316.html.
STATS 317. Stochastic Processes. 3 Units.
Semimartingales, stochastic integration, Ito's formula, Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric probability. Maxima of random fields and applications to spatial statistics and imaging.
STATS 318. Modern Markov Chains. 3 Units.
Tools for understanding Markov chains as they arise in applications. Random walk on graphs, reversible Markov chains, Metropolis algorithm, Gibbs sampler, hybrid Monte Carlo, auxiliary variables, hit and run, SwedsonWong algorithms, geometric theory, PoincareNashChegerLogSobolov inequalities. Comparison techniques, coupling, stationary times, Harris recurrence, central limit theorems, and large deviations.
STATS 319. Literature of Statistics. 13 Unit.
Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.
STATS 322. Function Estimation in White Noise. 23 Units.
Gaussian white noise model sequence space form. Hyperrectangles, quadratic convexity, and Pinsker's theorem. Minimax estimation on Lp balls and Besov spaces. Role of wavelets and unconditional bases. Linear and threshold estimators. Oracle inequalities. Optimal recovery and universal thresholding. Stein's unbiased risk estimator and threshold choice. Complexity penalized model selection. Connecting fast wavelet algorithms and theory. Beyond orthogonal bases.
STATS 325. Multivariate Analysis and Random Matrices in Statistics. 23 Units.
Topics on Multivariate Analysis and Random Matrices in Statistics (full description TBA).
STATS 330. An Introduction to Compressed Sensing. 3 Units.
Compressed sensing is a new data acquisition theory asserting that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data. This revelation may change the way engineers think about signal acquisition. Course covers fundamental theoretical ideas, numerical methods in largescale convex optimization, hardware implementations, connections with statistical estimation in high dimensions, and extensions such as recovery of data matrices from few entries (famous Netflix Prize).
Same as: CME 362
STATS 331. Survival Analysis. 2 Units.
The course introduces basic concepts, theoretical basis and statistical methods associated with survival data. Topics include censoring, KaplanMeier estimation, logrank test, proportional hazards regression, accelerated failure time model, multivariate failure time analysis and competing risks. The traditional counting process/martingale methods as well as modern empirical process methods will be covered. Prerequisite: Understanding of basic probability theory and statistical inference methods.
Same as: BIODS 231
STATS 333. Modern Spectral Analysis. 3 Units.
Traditional spectral analysis encompassed Fourier methods and their elaborations, under the assumption of a simple superposition of sinusoids, independent of time. This enables development of efficient and effective computational schemes, such as the FFT. Since many systems change in time, it becomes of interest to generalize classical spectral analysis to the timevarying setting. In addition, classical methods suffer from resolution limits which we hope to surpass. In this topics course, we follow two threads. On the one hand, we consider the ¿estimation of instantaneous frequencies and decomposition of source signals, which may be timevarying¿. The thread begins with the empirical mode decomposition (EMD) for nonstationary signal decomposition into intrinsic mode functions (IMF¿s), introduced by N. Huang et al [1], together with its machinery of the sifting process and computation of the Hilbert spectrum, resulting in the socalled adaptive harmonic model (AHM).nNext, this thread considers the wavelet synchrosqueezing transform (WSST) proposed by Daubechies et al [2], which attempts to estimate instantaneous frequencies (IF¿s), via the frequency reassignment (FRA) rule, that facilitaes nonstationary signal decomposition. In reference [3], a realtime method is proposed for computing the FRA rule; and in reference [4], the exact number of AHM components is determined with more precise estimation of the IF¿s, for more accurate extraction of the signal components and polynomiallike trend. nIn another thread, recent developments in optimization have been applied to obtain timevarying spectra or very highresolution spectra; in particular, references [5][8] give examples of recent results where convex estimation is applied to obtain new and more highly resolved spectral estimates, some with timevarying structure.
STATS 334. Mathematics and Statistics of Gambling. 3 Units.
Probability and statistics are founded on the study of games of chance. Nowadays, gambling (in casinos, sports and the Internet) is a huge business. This course addresses practical and theoretical aspects. Topics covered: mathematics of basic random phenomena (physics of coin tossing and roulette, analysis of various methods of shuffling cards), odds in popular games, card counting, optimal tournament play, practical problems of random number generation. Prerequisites: Statistics 116 and 200.
Same as: MATH 231
STATS 337. Readings in Applied Data Science. 3 Units.
Weekly readings and discussion of applied data science topics. Data wrangling, tidy data, and database basics. Visualization for exploration and explanation. The intersection of software engineering and data science: continuous integration, unit testing, and documentation. Reproducible research. Teaching data science. Prerequisites: competent at data analysis in R or Python. Please apply for the course by filling out the form at https://docs.google.com/forms/d/e/1FAIpQLSdz8hjIcMYlAv1rq37eAjWnNtflmOf1uOjrY4BhgJ3s4Td5kA/viewform Applications are due by Friday, March 23rd.
STATS 344. Introduction to Statistical Genetics. 3 Units.
Statistical methods for analyzing human genetics studies of Mendelian disorders and common complex traits. Probable topics include: principles of population genetics; epidemiologic designs; familial aggregation; segregation analysis; linkage analysis; linkagedisequilibriumbased association mapping approaches; and genomewide analysis based on highthroughput genotyping platforms. Prerequisite: STATS 116 or equivalent or consent of instructor.
Same as: GENE 244
STATS 345. Statistical and Machine Learning Methods for Genomics. 3 Units.
Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Same as: BIO 268, BIOMEDIN 245, CS 373, GENE 245
STATS 350. Topics in Probability Theory. 13 Unit.
See http://statweb.stanford.edu/~adembo/stat350/concentration/ Selected topics of contemporary research interest in probability theory. May be repeated once for credit. Prerequisite: 310A or equivalent.
STATS 351. Random Walks, Networks and Environment. 3 Units.
Selected material about probability on trees and networks, random walk in random and nonrandom environments, percolation and related interacting particle systems. Prerequisite: Exposure to measure theoretic probability and to stochastic processes.
STATS 351A. An Introduction to Random Matrix Theory. 3 Units.
Patterns in the eigenvalue distribution of typical large matrices, which also show up in physics (energy distribution in scattering experiments), combinatorics (length of longest increasing subsequence), first passage percolation and number theory (zeros of the zeta function). Classical compact ensembles (random orthogonal matrices). The tools of determinental point processes.
Same as: MATH 231A
STATS 355. Observational Studies. 23 Units.
This course will cover statistical methods for the design and analysis of observational studies. Topics for the course will include the potential outcomes framework for causal inference; randomized experiments; methods for controlling for observed confounders in observational studies; sensitivity analysis for hidden bias; instrumental variables; tests of hidden bias; coherence; and design of observational studies.
Same as: HRP 255
STATS 359. Topics in Mathematical Physics. 3 Units.
Covers a list of topics in mathematical physics. The specific topics may vary from year to year, depending on the instructor's discretion. Background in graduate level probability theory and analysis is desirable.
Same as: MATH 273
STATS 360. Advanced Statistical Methods for Earth System Analysis. 3 Units.
Introduction for graduate students to important issues in data analysis relevant to earth system studies. Emphasis on methodology, concepts and implementation (in R), rather than formal proofs. Likely topics include the bootstrap, nonparametric methods, regression in the presence of spatial and temporal correlation, extreme value analysis, timeseries analysis, highdimensional regressions and changepoint models. Topics subject to change each year. Prerequisites: STATS 110 or equivalent.
Same as: ESS 260
STATS 362. Topic: Monte Carlo. 3 Units.
Random numbers and vectors: inversion, acceptancerejection, copulas. Variance reduction: antithetics, stratification, control variates, importance sampling. MCMC: Markov chains, detailed balance, MetropolisHastings, random walk Metropolis,nnindependence sampler, Gibbs sampling, slice sampler, hybrids of Gibbs and Metropolis, tempering. Sequential Monte Carlo. QuasiMonte Carlo. Randomized quasiMonte Carlo. Examples, problems and motivation from Bayesian statistics,nnmachine learning, computational finance and graphics. May be repeat for credit.
STATS 363. Design of Experiments. 3 Units.
Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. BoxBehnken. Taguchi methods. Computer experiments and space filling designs. Prerequisites: probability at STATS 116 level or higher, and at least one course in linear models.
Same as: STATS 263
STATS 366. Modern Statistics for Modern Biology. 3 Units.
Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate methods (PCA and extensions), sparse representations (trees, networks, contingency tables) as well as nonparametric testing (Bootstrap, permutation and Monte Carlo methods). Hands on, use R and cover many Bioconductor packages. Prerequisite: Minimal familiarity with computers.
Same as: BIOS 221
STATS 367. Statistical Models in Genetics. 3 Units.
This course will cover statistical problems in population genetics and molecular evolution with an emphasis on coalescent theory. Special attention will be paid to current research topics, illustrating the challenges presented by genomic data obtained via highthroughput technologies. No prior knowledge of genomics is necessary. Familiarity with the R statistical package or other computing language is needed for homework assignments. Prerequisites: knowledge of probability through elementary stochastic processes and statistics through likelihood theory.
STATS 368. Empirical Process Theory and its Applications. 3 Units.
This course is on the theory of empirical processes. In the course we will focus on weak convergence of stochastic processes, Mestimation and empirical risk minimization. The course will cover topics like covering numbers and bracketing numbers, maximal inequalities, chaining and symmetrization, uniform law of large numbers and uniform central limit theorems, rates of convergence of MLEs and (penalized) least squares estimators, and concentration inequalities.
STATS 369. Methods from Statistical Physics. 3 Units.
Mathematical techniques from statistical physics have been applied with increasing success on problems form combinatorics, computer science, machine learning. These methods are nonrigorous, but in several cases they were proved to yield correct predictions. This course provides a working knowledge of these methods for nonphysicists. Specific topics: the SherringtonKirkpatrick model; sparse regression with random designs;.
STATS 370. A Course in Bayesian Statistics. 3 Units.
This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference.
Same as: STATS 270
STATS 371. Applied Bayesian Statistics. 3 Units.
This course is a modern treatment of applied Bayesian statistics with a focus on highdimensional problems. We will study a collection of canonical methods that see heavy use in applications, including highdimensional linear and generalized linear models, hierarchical/random effects models, Gaussian processes, variabledimension and Dirichlet process mixtures, graphical models, and methods used in Bayesian inverse problems. Each method will be accompanied by one or more motivating datasets. Through these examples the course will cover: (1) Bayesian hypothesis testing, multiplicity correction, selection, shrinkage, and model averaging; (2) prior choice; (3) Frequentist properties of Bayesian procedures in high dimensions; and (4) computation by Markov chain Monte Carlo, including constructing efficient Gibbs, Metropolis, and more exotic samplers, empirical convergence analysis, strategies for scaling computation to high dimensions (approximations, divideandconquer, minibatching, et cetera), and the theory of convergence rates.
Same as: STATS 271
STATS 374. Large Deviations Theory. 3 Units.
Combinatorial estimates and the method of types. Large deviation probabilities for partial sums and for empirical distributions, Cramer's and Sanov's theorems and their Markov extensions. Applications in statistics, information theory, and statistical mechanics. Prerequisite: MATH 230A or STATS 310. Offered every 23 years. http://statweb.stanford.edu/~adembo/largedeviations/.
Same as: MATH 234
STATS 376A. Information Theory. 3 Units.
Projectbased course about how to measure, represent, and communicate information effectively. Why bits have become the universal currency for information exchange. How information theory bears on the design and operation of modernday systems such as smartphones and the Internet. The role of entropy and mutual information in data compression, communication, and inference. Practical compressors and error correcting codes. The information theoretic way of thinking. Relations and applications to probability, statistics, machine learning, biological and artificial neural networks, genomics, quantum information, and blockchains. Prerequisite: a first undergraduate course in probability.
Same as: EE 376A
STATS 376B. Topics in Information Theory and Its Applications. 3 Units.
Information theory establishes the fundamental limits on compression and communication over networks. The tools of information theory have also found applications in many other fields, including probability and statistics, computer science and physics. The course will cover selected topics from these applications, including communication networks, through regular lectures and student projects. Prerequisites: EE376A.
Same as: EE 376B
STATS 385. Theories of Deep Learning. 1 Unit.
The spectacular recent successes of deep learning are purely empirical. Nevertheless intellectuals always try to explain important developments theoretically. In this literature course we will review recent work of Burna and Mallat, Mhaskar and Poggio, Papyan and Elad, Bolsckei and coauthors, Baraniuk and coauthors, and others, seeking to build theoretical frameworks deriving deep networks as consequences. After initial background lectures, we will have some of the authors presenting lectures on specific papers.
STATS 390. Consulting Workshop. 13 Unit.
Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's dropin consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: course work in applied statistics or data analysis, and consent of instructor.
STATS 397. PhD Oral Exam Workshop. 1 Unit.
For Statistics PhD students defending their dissertation.
STATS 398. Industrial Research for Statisticians. 1 Unit.
Doctoral research as in 399, but must be conducted for an offcampus employer. A final report acceptable to the advisor outlining work activity, problems investigated, key results, and any followup projects they expect to perform is required. The report is due at the end of the quarter in which the course is taken. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate.
STATS 399. Research. 110 Unit.
Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit.
STATS 801. TGR Project. 0 Units.
.
STATS 802. TGR Dissertation. 0 Units.
.