STATS 270: Bayesian Statistics I (STATS 370)
This is the first of a two course sequence on modern Bayesian statistics. Topics covered include: real world examples of large scale Bayesian analysis; basic tools (models, conjugate priors and their mixtures); Bayesian estimates, tests and credible intervals; foundations (axioms, exchangeability, likelihood principle); Bayesian computations (Gibbs sampler, data augmentation, etc.); prior specification. Prerequisites: statistics and probability at the level of
Stats300A,
Stats305, and
Stats310.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wong, W. (PI)
STATS 305A: Introduction to Statistical Modeling
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and GaussMarkov theorem. Computation via QR decomposition, GrammSchmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage & influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. 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).
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Palacios, J. (PI)
;
Chin, A. (TA)
;
Greaves, D. (TA)
...
more instructors for STATS 305A »
Instructors:
Palacios, J. (PI)
;
Chin, A. (TA)
;
Greaves, D. (TA)
;
Ignatiadis, N. (TA)
;
Rosenman, E. (TA)
;
Sohn, Y. (TA)
STATS 305B: Methods for Applied Statistics I: Exponential Families in Theory and Practice
Exponential families are central to parametric statistical inference. This course emphasizes the applied aspects of exponential family theory, with special emphasis on Generalized Linear Models. Prerequisite: 305A or equivalent. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Efron, B. (PI)
STATS 305C: Methods for Applied Statistics II: Applied Multivariate Statistics
Theory, computational aspects, and practice of a variety of important multivariate statistical tools for data analysis. Topics include classical multivariate Gaussian and undirected graphical models, graphical displays. PCA, SVD and generalizations including canonical correlation analysis, linear discriminant analysis, correspondence analysis, with focus on recent variants. Factor analysis and independent component analysis. Topic modeling. Multidimensionalnscaling and its variants (e.g. Isomap, spectral clustering). Matrix completion. nStudents will be expected to program  ideally in R. Prerequisites: Stats305a and
Stats 305b or equivalent. (NB: prior to 201617 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Hastie, T. (PI)
STATS 370: Bayesian Statistics I (STATS 270)
This is the first of a two course sequence on modern Bayesian statistics. Topics covered include: real world examples of large scale Bayesian analysis; basic tools (models, conjugate priors and their mixtures); Bayesian estimates, tests and credible intervals; foundations (axioms, exchangeability, likelihood principle); Bayesian computations (Gibbs sampler, data augmentation, etc.); prior specification. Prerequisites: statistics and probability at the level of
Stats300A,
Stats305, and
Stats310.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wong, W. (PI)
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