## STATS 264: Foundations of Statistical and Scientific Inference (HRP 264)

The course will consist of readings and discussion of foundational papers and book sections in the domains of statistical and scientific inference. Topics to be covered include philosophy of science, interpretations of probability, Bayesian and frequentist approaches to statistical inference and current controversies about the proper use of p-values and research reproducibility. nnRecommended preparation: At least 2 quarters of biostatistics and one of epidemiology. Intended for second year Masters students, of PhD students with as least 1 year of preceding graduate training.

Terms: Aut
| Units: 1

Instructors:
Goodman, S. (PI)

## 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: 2

Instructors:
Donoho, D. (PI)

## 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 for credit

Instructors:
Ioannidis, J. (PI)
;
Lai, T. (PI)
;
Rothenhaeusler, D. (PI)
...
more instructors for STATS 298 »

Instructors:
Ioannidis, J. (PI)
;
Lai, T. (PI)
;
Rothenhaeusler, D. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)

## 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)
;
Duchi, J. (PI)
;
Efron, B. (PI)
...
more instructors for STATS 299 »

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Ioannidis, J. (PI)
;
Lai, T. (PI)
;
Rogosa, D. (PI)
;
Rothenhaeusler, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)

## 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: 3

Instructors:
Rothenhaeusler, D. (PI)

## STATS 303: PhD First/Second Year Student Workshop

For Statistics first and second year PhD students only. Discussion of statistics topics and research areas; consultation with PhD advisors.

Terms: Aut
| Units: 1
| Repeatable for credit

Instructors:
Owen, A. (PI)

## 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: 3

Instructors:
Owen, A. (PI)

## 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: 3

Instructors:
Montanari, A. (PI)

## 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: Aut
| Units: 3
| Repeatable for credit

Instructors:
Lai, T. (PI)

## 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
| Repeatable for credit

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