STATS 32:
Introduction to R for Undergraduates
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 application-driven. 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 non-engineering students. Laptops necessary for use in class.
Terms: Aut
| Units: 1
STATS 48N:
Riding the Data Wave
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.
Last offered: Autumn 2016
| Units: 3
| UG Reqs: WAY-AQR, WAY-FR
STATS 60:
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors: ;
Baiocchi, M. (PI);
Miolane, N. (PI);
Poldrack, R. (PI);
Xia, L. (PI);
Azadkia, M. (TA);
Cao, S. (TA);
Guo, K. (TA);
Han, K. (TA);
Lemhadri, I. (TA);
Ruan, F. (TA);
Samyak, R. (TA);
Wu, Y. (TA)
STATS 101:
Data Science 101
http://web.stanford.edu/class/stats101/ . This course will provide a hands-on 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 real-world 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.
Terms: Aut, Spr
| Units: 5
| UG Reqs: GER: DB-NatSci, WAY-AQR
STATS 110:
Statistical Methods in Engineering and the Physical Sciences
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.
Terms: Aut, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
STATS 110U:
Statistical Methods in Engineering and the Physical Sciences
For Summer UG Visitors only. Same as 110. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum
| Units: 5
STATS 116:
Theory of Probability
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.
Terms: Aut, Spr, Sum
| Units: 4
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors: ;
Kaluwa Devage, P. (PI);
Mohanty, P. (PI);
Siegmund, D. (PI);
Zhu, X. (PI);
Bhattacharya, S. (TA);
Bi, N. (TA);
Cao, S. (TA);
Li, S. (TA);
Misiakiewicz, T. (TA);
SUR, P. (TA);
Wu, H. (TA);
Xu, H. (TA)
STATS 116U:
Theory of Probability
For Summer UG Visitors only. Same as Stats 116. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum
| Units: 4
STATS 141:
Biostatistics (BIO 141)
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.
Terms: Aut
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR
STATS 155:
Statistical Methods in Computational Genetics
The computational methods necessary for the construction and evaluation of sequence alignments and phylogenies built from molecular data and genetic data such as micro-arrays 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)
Terms: Aut
| Units: 3
STATS 160:
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
Instructors: ;
Baiocchi, M. (PI);
Miolane, N. (PI);
Poldrack, R. (PI);
Xia, L. (PI);
Azadkia, M. (TA);
Cao, S. (TA);
Guo, K. (TA);
Han, K. (TA);
Lemhadri, I. (TA);
Ruan, F. (TA);
Samyak, R. (TA);
Wu, Y. (TA)
STATS 191:
Introduction to Applied Statistics
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. 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.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-Math, WAY-AQR
STATS 195:
Introduction to R (CME 195)
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 application-driven. No prior programming experience is assumed.
Terms: Aut, Spr
| Units: 1
STATS 196A:
Multilevel Modeling Using R (EDUC 401D)
See http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, three-level analyses. Class meets April 10, April 17, April 24, May 1, May 15.
Terms: Spr
| Units: 1
STATS 199:
Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
STATS 200:
Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116. http://statweb.stanford.edu/~owen/courses/200
Terms: Aut, Win
| Units: 3
Instructors: ;
Mohanty, P. (PI);
Owen, A. (PI);
Palacios, J. (PI);
Ghosh, S. (TA);
Han, K. (TA);
Li, S. (TA);
Pavlyshyn, D. (TA);
Ren, Z. (TA);
Roquero Gimenez, J. (TA);
Samyak, R. (TA);
Tsao, A. (TA);
Wu, H. (TA);
Wu, Y. (TA)
STATS 202:
Data Mining and Analysis
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).
Terms: Aut, Sum
| Units: 3
Instructors: ;
Taylor, J. (PI);
Tran, L. (PI);
Cai, F. (TA);
Feldman, M. (TA);
GAO, Z. (TA);
Guo, K. (TA);
Han, K. (TA);
Markovic, J. (TA);
Miao, J. (TA);
Qian, J. (TA);
Ray, S. (TA);
Rosenbaum, A. (TA);
Tirlea, M. (TA);
Wang, X. (TA);
Wu, H. (TA)
STATS 202U:
Data Mining and Analysis
For Summer UG Visitors only. Sames as Stats 202. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum
| Units: 3
STATS 203:
Introduction to Regression Models and Analysis of Variance
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.
Terms: Spr
| Units: 3
STATS 203V:
Introduction to Regression Models and Analysis of Variance
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 Zoom. Prerequisites: a post-calculus introductory probability course, e.g. STATS 116. In addition, a co-requisite post-calculus mathematical statistics course, e.g. STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.
Terms: Sum
| Units: 3
STATS 204:
Sampling
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.
Terms: Spr
| Units: 3
STATS 205:
Introduction to Nonparametric Statistics
Nonparametric analogs of the one- and two-sample t-tests and analysis of variance; the sign test, median test, Wilcoxon's tests, and the Kruskal-Wallis and Friedman tests, tests of independence. Nonparametric regression and nonparametric density estimation, modern nonparametric techniques, nonparametric confidence interval estimates.
Terms: Spr
| Units: 3
STATS 206:
Applied Multivariate Analysis
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive 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.
Terms: Win
| Units: 3
STATS 207:
Introduction to Time Series Analysis
Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.
Terms: Spr
| Units: 3
STATS 208:
Introduction to the Bootstrap
The bootstrap is a computer-based 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, cross-validation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability.
Terms: Win
| Units: 3
STATS 209:
Statistical Methods for Group Comparisons and Causal Inference (EDUC 260A, HRP 239)
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: intermediate-level statistical methods.
Terms: Win
| Units: 3
STATS 211:
Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, HRP 206, MED 206)
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis 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 meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win
| Units: 3
STATS 212:
Applied Statistics with SAS
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.
Last offered: Summer 2011
| Units: 3
STATS 213:
Introduction to Graphical Models (STATS 313)
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.
Last offered: Winter 2015
| Units: 3
STATS 215:
Statistical Models in Biology
Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher 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.
Terms: Spr
| Units: 3
STATS 216:
Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered via video segments (MOOC style), and in-class 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).
Terms: Win
| Units: 3
STATS 216V:
Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. 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). May not be taken for credit by students with credit in STATS 202 or STATS 216.
Terms: Sum
| Units: 3
STATS 217:
Introduction to Stochastic Processes I
Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking STATS 215 instead. Prerequisite: a post-calculus introductory probability course e.g. STATS 116
Terms: Win, Sum
| Units: 3
STATS 218:
Introduction to Stochastic Processes II
Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.
Terms: Spr
| Units: 3
STATS 219:
Stochastic Processes (MATH 136)
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/math-136/
Terms: Win
| Units: 3
STATS 222:
Statistical Methods for Longitudinal Research (EDUC 351A)
See http://rogosateaching.com/stat222/. Research designs and statistical procedures for time-ordered (repeated-measures) 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 non-experimental group comparisons, reciprocal effects, stability. Prerequisite: intermediate statistical methods
Terms: Aut
| Units: 2-3
STATS 229:
Machine Learning (CS 229)
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric 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.
Terms: Aut, Spr, Sum
| Units: 3-4
Instructors: ;
Avati, A. (PI);
Ma, T. (PI);
Ng, A. (PI);
Re, C. (PI);
Amidi, S. (TA);
Avati, A. (TA);
Bakr, S. (TA);
Batra, S. (GP);
Bereket, M. (TA);
Bhaskhar, N. (TA);
Chinchali, S. (TA);
Chute, C. (TA);
Colas, G. (TA);
Dao, T. (TA);
Duan, T. (TA);
Dwaracherla, V. (TA);
Farhangi, A. (TA);
Gao, X. (TA);
Geng, Z. (TA);
Hua, X. (TA);
Huot, F. (TA);
Jung, S. (TA);
Kaur, J. (TA);
Kim, A. (TA);
Kim, C. (TA);
Koochak, Z. (TA);
Li, H. (TA);
Lin, C. (TA);
Liu, C. (TA);
Pandey, S. (TA);
Parulekar, A. (TA);
Paul, S. (TA);
Ramamoorthy, A. (TA);
Song, R. (TA);
Srouji, M. (TA);
Starosta, A. (TA);
Steinberg, E. (TA);
Townshend, R. (TA);
Wang, C. (TA);
Wang, Q. (TA);
Wang, Y. (TA);
Wu, X. (TA);
Yeh, C. (TA);
Yerukola, A. (TA);
Zhang, J. (TA)
STATS 231:
Statistical Learning Theory (CS 229T)
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).
Terms: Aut
| Units: 3
STATS 237:
Investment Portfolios, Derivative Securities, and Risk Measures
Asset returns and their volatilities. Markowitz portfolio theory, capital asset pricing model, multifactor pricing models. Measures of market risk and statistical models and methods for their estimation and backtesting. Financial derivatives and hedging. Black-Scholes pricing of European options and implied volatilities. Prerequisite: STATS 116 or equivalent.
Terms: Sum
| Units: 3
STATS 237P:
Investment Portfolios, Derivative Securities, and Risk Measures
For SCPD students; see STATS237
Terms: Sum
| Units: 3
STATS 240:
Statistical Methods in Finance
(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.
Terms: Aut
| Units: 2-4
STATS 240P:
Statistical Methods in Finance
For SCPD students; see 240.
Terms: Aut
| Units: 3
STATS 241:
Data-driven Financial Econometrics
(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.
Terms: Sum
| Units: 3
STATS 241P:
Data-driven Financial Econometrics
For SCPD students; see STATS241.
Terms: Sum
| Units: 3
STATS 243:
Risk Analytics and Management in Finance and Insurance (CME 243)
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.
Last offered: Summer 2016
| Units: 2-4
STATS 243P:
Risk Analytics and Management in Finance and Insurance
For SCPD students; see STATS243.
Last offered: Winter 2015
| Units: 3
STATS 244:
Quantitative Trading: Algorithms, Data, and Optimization
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.
Last offered: Autumn 2016
| Units: 2-4
STATS 244P:
Quantitative Trading: Algorithms, Data and Optimization
For SCPD students; see 244.
Last offered: Autumn 2016
| Units: 3
STATS 245:
Data, Models and Applications to Healthcare Analytics
Topics on fundamentals of data science, biological and statistical models, application to medical product safety evaluation, health risk models and their evaluation, benefit-risk assessment and multi-criteria 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.
Terms: Sum
| Units: 3
STATS 245P:
Data, Models, and Applications to Healthcare Analytics
For SCPD students; see STATS245.
Terms: Sum
| Units: 3
STATS 248:
Clinical Trial Design in the Age of Precision Medicine and Health (BIODS 248)
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.
Terms: Aut
| Units: 2-4
STATS 248P:
Clinical Trial Design in the Age of Precision Medicine and Health
For SCPD students; others see STATS 248
Terms: Aut
| Units: 3
STATS 250:
Mathematical Finance (MATH 238)
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.
Terms: Win
| Units: 3
STATS 260A:
Workshop in Biostatistics (BIODS 260A)
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.
Terms: Aut
| Units: 1-2
| Repeatable
for credit
STATS 260B:
Workshop in Biostatistics (BIODS 260B)
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.
Terms: Win
| Units: 1-2
| Repeatable
for credit
STATS 260C:
Workshop in Biostatistics (BIODS 260C)
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.
Terms: Spr
| Units: 1-2
| Repeatable
for credit
STATS 261:
Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261)
Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
Terms: Win
| Units: 3
STATS 262:
Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)
Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.
Terms: Spr
| Units: 3
STATS 263:
Design of Experiments (STATS 363)
Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. Box-Behnken. Taguchi methods. Computer experiments and space filling designs. Prerequisites: probability at STATS 116 level or higher, and at least one course in linear models.
Last offered: Winter 2017
| Units: 3
STATS 266:
Advanced Statistical Methods for Observational Studies (CHPR 266, EDUC 260B, HRP 292)
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. Pre-requisites: HRP 261 and 262 or STATS 209 (HRP 239), or equivalent. See http://rogosateaching.com/somgen290/
Terms: Spr
| Units: 2-3
STATS 270:
A Course in Bayesian Statistics (STATS 370)
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.
Terms: Win
| Units: 3
STATS 271:
Applied Bayesian Statistics (STATS 371)
This course is a modern treatment of applied Bayesian statistics with a focus on high-dimensional problems. We will study a collection of canonical methods that see heavy use in applications, including high-dimensional linear and generalized linear models, hierarchical/random effects models, Gaussian processes, variable-dimension 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, divide-and-conquer, minibatching, et cetera), and the theory of convergence rates.
Terms: Spr
| Units: 3
STATS 285:
Massive Computational Experiments, Painlessly
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 state-of-the-art 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 nuts-and-bolts 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 high-level computer language such as R, Matlab, or Python.
Terms: Aut
| Units: 1-2
STATS 290:
Computing for Data Science
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.
Terms: Win
| Units: 3
STATS 298:
Industrial Research for Statisticians
Masters-level research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an off-campus 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.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
3 times
(up to 3 units total)
STATS 299:
Independent Study
For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-5
| Repeatable
for credit
Instructors: ;
Baiocchi, M. (PI);
Chatterjee, S. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Ma, T. (PI);
Narasimhan, B. (PI);
Olshen, R. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Wager, S. (PI)
STATS 300:
Advanced Topics in Statistics
TBD
Last offered: Summer 2018
| Units: 2-3
| Repeatable
for credit
STATS 300A:
Theory of Statistics I
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.
Terms: Aut
| Units: 2-3
STATS 300B:
Theory of Statistics II
Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: Neyman-Pearson 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.
Terms: Win
| Units: 2-4
STATS 300C:
Theory of Statistics III
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.
Terms: Spr
| Units: 2-4
STATS 302:
Qualifying Exams Workshop
Prepares Statistics Ph.D. students for the qualifying exams by reviewing relevant course topics and problem solving strategies.
Terms: Sum
| Units: 3
STATS 303:
PhD First Year Student Workshop
For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
4 times
(up to 4 units total)
STATS 305A:
Applied Statistics I
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, Gauss-Markov. Gram-Schmidt, 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 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Aut
| Units: 2-3
STATS 305B:
Applied Statistics II
Methods for discrete responses. Logistic regression. Poisson loglinear models. Probit model. Generalized linear models. Exponential families. Overdispersion. Tests of independence. Cox proportional hazards model. Bradley-Terry models. Cochran-Mantel-Haenszel. 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 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Win
| Units: 2-3
STATS 305C:
Applied Statistics III
Methods for multivariate responses. Theory, computation and practice for multivariate statistical tools. Multivariate Gaussian and undirected graphical models, graphical displays. Hotelling¿s T-squared, principal components, canonical correlations, linear discriminant analysis, correspondence analysis, and recent variants of these. Hierarchical and k-means clustering. Bi-clustering. Factor analysis and independent component analysis. Topic modeling. Multidimensional scaling and variants (e.g., Isomap, spectral clustering, t-SNE). 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 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Spr
| Units: 2-3
STATS 310A:
Theory of Probability I (MATH 230A)
Mathematical tools: sigma algebras, measure theory, connections between coin tossing and Lebesgue measure, basic convergence theorems. Probability: independence, Borel-Cantelli 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.
Terms: Aut
| Units: 2-4
STATS 310B:
Theory of Probability II (MATH 230B)
Conditional expectations, discrete time martingales, stopping times, uniform integrability, applications to 0-1 laws, Radon-Nikodym 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.
Terms: Win
| Units: 2-3
STATS 310C:
Theory of Probability III (MATH 230C)
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/stat-310c/
Terms: Spr
| Units: 2-4
STATS 311:
Information Theory and Statistics (EE 377)
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, f-divergences. Penalized estimators and minimumndescription length. Online game playing, gambling, no-regret learning. Prerequisites: EE 376A (or equivalent) or STATS 300A.
Terms: Win
| Units: 3
STATS 312:
Statistical Methods in Neuroscience
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 single-neuron and neuroimaging technologies, statistical models for single response, encoding and decoding models, multiple-responses 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.
Last offered: Winter 2015
| Units: 3
STATS 313:
Introduction to Graphical Models (STATS 213)
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.
Last offered: Winter 2015
| Units: 3
STATS 314A:
Advanced Statistical Theory
Covers a range of topics, including: empirical processes, asymptotic efficiency, uniform convergence of measures, contiguity, resampling methods, Edgeworth expansions.
Terms: Spr
| Units: 3
| Repeatable
for credit
STATS 314B:
Topics in Minimax Inference of Nonparametric Functionals
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 U-statistics.
Last offered: Spring 2016
| Units: 3
STATS 315A:
Modern Applied Statistics: Learning
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.
Terms: Win
| Units: 2-3
STATS 315B:
Modern Applied Statistics: Data Mining
Two-part 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.
Terms: Spr
| Units: 2-3
Instructors: ;
Friedman, J. (PI);
Achanta, R. (TA);
Hwang, J. (TA);
Katsevich, G. (TA);
Miao, J. (TA);
Qian, J. (TA);
SUR, P. (TA);
Sesia, M. (TA);
Wang, X. (TA);
Zhong, C. (TA)
STATS 316:
Stochastic Processes on Graphs
Local weak convergence, Gibbs measures on trees, cavity method, and replica symmetry breaking. Examples include random k-satisfiability, the assignment problem, spin glasses, and neural networks. Prerequisite: 310A or equivalent. https://web.stanford.edu/~montanar/TEACHING/Stat316/stat316.html
Last offered: Autumn 2017
| Units: 1-3
STATS 317:
Stochastic Processes
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.
Last offered: Winter 2018
| Units: 3
STATS 318:
Modern Markov Chains
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, Swedson-Wong algorithms, geometric theory, Poincare-Nash-Cheger-Log-Sobolov inequalities. Comparison techniques, coupling, stationary times, Harris recurrence, central limit theorems, and large deviations.
Terms: Spr
| Units: 3
STATS 319:
Literature of Statistics
Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1-3
| Repeatable
for credit
STATS 322:
Function Estimation in White Noise
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.
Last offered: Autumn 2015
| Units: 2-3
STATS 325:
Multivariate Analysis and Random Matrices in Statistics
Topics on Multivariate Analysis and Random Matrices in Statistics (full description TBA)
Terms: Spr
| Units: 2-3
STATS 330:
An Introduction to Compressed Sensing (CME 362)
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 large-scale 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).
Last offered: Autumn 2014
| Units: 3
STATS 331:
Survival Analysis (BIODS 231)
The course introduces basic concepts, theoretical basis and statistical methods associated with survival data. Topics include censoring, Kaplan-Meier 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.
Last offered: Spring 2017
| Units: 2
STATS 333:
Modern Spectral Analysis
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 time-varying 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 time-varying¿. The thread begins with the empirical mode decomposition (EMD) for non-stationary 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 so-called 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 re-assignment (FRA) rule, that facilitaes non-stationary signal decomposition. In reference [3], a real-time 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 polynomial-like trend. nIn another thread, recent developments in optimization have been applied to obtain time-varying spectra or very high-resolution 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 time-varying structure.
Last offered: Spring 2015
| Units: 3
STATS 334:
Mathematics and Statistics of Gambling (MATH 231)
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.
Last offered: Spring 2018
| Units: 3
STATS 337:
Readings in Applied Data Science
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.
Last offered: Spring 2018
| Units: 3
STATS 344:
Introduction to Statistical Genetics (GENE 244)
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; linkage-disequilibrium-based association mapping approaches; and genome-wide analysis based on high-throughput genotyping platforms. Prerequisite: STATS 116 or equivalent or consent of instructor.
Last offered: Spring 2014
| Units: 3
STATS 345:
Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, CS 373, GENE 245)
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.
Last offered: Spring 2018
| Units: 3
STATS 350:
Topics in Probability Theory
See http://statweb.stanford.edu/~adembo/stat-350/concentration/ Selected topics of contemporary research interest in probability theory. May be repeated once for credit. Prerequisite: 310A or equivalent.
Last offered: Autumn 2017
| Units: 1-3
| Repeatable
2 times
(up to 6 units total)
STATS 351:
Random Walks, Networks and Environment
Selected material about probability on trees and networks, random walk in random and non-random environments, percolation and related interacting particle systems. Prerequisite: Exposure to measure theoretic probability and to stochastic processes.
Last offered: Spring 2014
| Units: 3
STATS 359:
Topics in Mathematical Physics (MATH 273)
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.
Terms: Aut
| Units: 3
| Repeatable
for credit
STATS 362:
Topic: Monte Carlo
Random numbers and vectors: inversion, acceptance-rejection, copulas. Variance reduction: antithetics, stratification, control variates, importance sampling. MCMC: Markov chains, detailed balance, Metropolis-Hastings, random walk Metropolis,nnindependence sampler, Gibbs sampling, slice sampler, hybrids of Gibbs and Metropolis, tempering. Sequential Monte Carlo. Quasi-Monte Carlo. Randomized quasi-Monte Carlo. Examples, problems and motivation from Bayesian statistics,nnmachine learning, computational finance and graphics. May be repeat for credit.
Last offered: Winter 2018
| Units: 3
| Repeatable
2 times
(up to 6 units total)
STATS 363:
Design of Experiments (STATS 263)
Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. Box-Behnken. Taguchi methods. Computer experiments and space filling designs. Prerequisites: probability at STATS 116 level or higher, and at least one course in linear models.
Last offered: Winter 2017
| Units: 3
STATS 366:
Modern Statistics for Modern Biology (BIOS 221)
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.
Terms: Sum
| Units: 3
STATS 367:
Statistical Models in Genetics
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 high-throughput 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.
Last offered: Winter 2018
| Units: 3
STATS 368:
Empirical Process Theory and its Applications
This course is on the theory of empirical processes. In the course we will focus on weak convergence of stochastic processes, M-estimation 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.
Last offered: Spring 2017
| Units: 3
STATS 369:
Methods from Statistical Physics
Mathematical techniques from statistical physics have been applied with increasing success on problems form combinatorics, computer science, machine learning. These methods are non-rigorous, but in several cases they were proved to yield correct predictions. This course provides a working knowledge of these methods for non-physicists. Specific topics: the Sherrington-Kirkpatrick model; sparse regression with random designs;
Last offered: Winter 2017
| Units: 3
STATS 370:
A Course in Bayesian Statistics (STATS 270)
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.
Terms: Win
| Units: 3
STATS 371:
Applied Bayesian Statistics (STATS 271)
This course is a modern treatment of applied Bayesian statistics with a focus on high-dimensional problems. We will study a collection of canonical methods that see heavy use in applications, including high-dimensional linear and generalized linear models, hierarchical/random effects models, Gaussian processes, variable-dimension 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, divide-and-conquer, minibatching, et cetera), and the theory of convergence rates.
Terms: Spr
| Units: 3
STATS 374:
Large Deviations Theory (MATH 234)
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 2-3 years. http://statweb.stanford.edu/~adembo/large-deviations/
Terms: Spr
| Units: 3
STATS 376A:
Information Theory (EE 376A)
Project-based 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 modern-day 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.
Terms: Win
| Units: 3
STATS 376B:
Topics in Information Theory and Its Applications (EE 376B)
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
Terms: Spr
| Units: 3
STATS 385:
Theories of Deep Learning
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 co-authors, Baraniuk and co-authors, 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.
Last offered: Autumn 2017
| Units: 1
STATS 390:
Consulting Workshop
Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in 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.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
for credit
STATS 397:
PhD Oral Exam Workshop
For Statistics PhD students defending their dissertation.
Terms: Spr
| Units: 1
STATS 398:
Industrial Research for Statisticians
Doctoral research as in 399, but must be conducted for an off-campus employer. A final report acceptable to the advisor outlining work activity, problems investigated, key results, and any follow-up 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.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
for credit
Instructors: ;
Candes, E. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Montanari, A. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Romano, J. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Wager, S. (PI);
Walther, G. (PI);
Wong, W. (PI)
STATS 399:
Research
Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit.
Terms: Aut, Win, Spr, Sum
| Units: 1-10
| Repeatable
for credit
Instructors: ;
Baiocchi, M. (PI);
Candes, E. (PI);
Chatterjee, S. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Liang, P. (PI);
Linderman, S. (PI);
Mackey, L. (PI);
Montanari, A. (PI);
Mukherjee, R. (PI);
Narasimhan, B. (PI);
Owen, A. (PI);
Palacios, J. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Wager, S. (PI);
Walther, G. (PI);
Wong, W. (PI)
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Candes, E. (PI);
Chatterjee, S. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Montanari, A. (PI);
Owen, A. (PI);
Rajaratnam, B. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tibshirani, R. (PI);
Walther, G. (PI);
Wong, W. (PI)
STATS 802:
TGR Dissertation
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
Instructors: ;
Candes, E. (PI);
Chatterjee, S. (PI);
Dembo, A. (PI);
Diaconis, P. (PI);
Donoho, D. (PI);
Duchi, J. (PI);
Duffie, D. (PI);
Efron, B. (PI);
Friedman, J. (PI);
Hastie, T. (PI);
Holmes, S. (PI);
Johnstone, I. (PI);
Lai, T. (PI);
Liang, P. (PI);
Montanari, A. (PI);
Owen, A. (PI);
Rogosa, D. (PI);
Romano, J. (PI);
Sabatti, C. (PI);
Siegmund, D. (PI);
Switzer, P. (PI);
Taylor, J. (PI);
Tian, L. (PI);
Tibshirani, R. (PI);
Walther, G. (PI);
Wong, W. (PI)
