printer friendly pageSTATS 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
Instructors:
Wong, W. (PI)
;
Li, D. (TA)
STATS 305A: Introduction to Statistical Modeling
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and Gauss-Markov theorem. Computation via QR decomposition, Gramm-Schmidt 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 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Aut
| Units: 3
Instructors:
Owen, A. (PI)
;
Guan, L. (TA)
;
Le, Y. (TA)
;
Markovic, J. (TA)
;
Powers, S. (TA)
;
Rosenman, E. (TA)
STATS 305B: Methods for Applied Statistics I
Regression modeling extended to categorical data. Logistic regression. Loglinear models. Generalized linear models. Discriminant analysis. Categorical data models from information retrieval and Internet modeling. Prerequisite: 305A or equivalent. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Win
| Units: 3
STATS 305C: Methods for Applied Statistics II: Applied Bayesian Statistics
Applied Bayesian statistics. Fundamentals, hierarchical models, computing. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Spr
| Units: 3
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
Instructors:
Wong, W. (PI)
;
Li, D. (TA)
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