Stats 201C Advanced Modeling and Inference
Main topics:
(1)
Bayesian hierarchical models, prior and posterior distributions, hyper-priors,
posterior predictive distribution, shrinkage.
(2) Missing data problems, the EM algorithm, data augmentation,
incomplete multivariate normal data.
(3) Mixture modeling, clustering
algorithms, Dirichlet process.
(4) Hidden Markov models, dynamic programming, forward
summation and backward sampling, Kalman filter.
(5) Bayesian networks, graphical model, DAGs, belief propagation, structural
inference, model selection and model averaging.
Instructor: Qing Zhou, Department of Statistics, zhou@stat.ucla.edu
References
1) Gelman, A. et al. Bayesian data analysis.
(Second edition, 2004).
Lectures: MWF 12-12:50pm, MS 5203.
Office Hours: Wed 4-6pm, MS 8979.
TA: Gong Chen, gongchen@ucla.edu, Office Hours: Thursday 4-6pm, MS
8359.
2) Schafer, J.L. Analysis of incomplete
multivariate data. (First edition1997)
3) Rabiner, L.R. (1989) A tutorial on hidden Markov
models and selected applications in speech recognition. Proceedings of the IEEE,
77: 257-286.
4) Pearl, J. Causality: Models, Reasoning and Inference. (2000) UK: Cambridge
University Press.
Lecture Notes: 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b
Homework: Hw1 (Dataset 1) Hw2 Hw3 (Dataset 2) Hw4 (Dataset 3) Hw5 (Dataset 4)
Homework solutions: Hw1 Hw2 Hw3 Hw4 HW5
R-code for selected HW problems: Hw1Q3 Hw2Q2 Hw3Q3 Hw4Q2,Q3 Hw5Q2