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Nonparametric Bayesian methods (Dirichlet processes) Lecturer: Kurt Miller Date: Nov 19 [Lecture slides] References There are numerous references on Bayesian methods and Markov Chain Monte Carlo (MCMC) techniques. Three useful textbooks are: Bayesian Data Analaysis. Gelman, Carlin, Stern, Rubin. The Bayesian Choice. Robert. Monte Carlo Statistical Methods. Robert, Casella. Unfortunately, there cur

Stat 260/CS 294 Bayesian Modeling and Inference Prof. Michael Jordan Monday and Wednesday, 1:30-3:00, 330 Evans Spring 2010 Announcements 4/28: Homework 5 is due on May 5th at 5pm. Hardcopies only accepted; they should be placed in the box outside my office door (427 Evans). 4/22: On problem 1 in Homework 5, you can assume that the kernel satisfies detailed balance. 4/21: The due date for the fina

header.tex Introduction [ps] [pdf] Maximal margin classification [ps] [pdf] Introduction to kernels [ps] [pdf] Ridge regression and kernels [ps] [pdf] Properties of kernels [ps] [pdf] Soft-margin SVM, sparseness [ps] [pdf] Regression, the SVD and PCA [ps] [pdf] Kernel PCA and kernel CCA Incomplete Cholesky decomposition [ps] [pdf] ANOVA kernels and diffusion kernels [ps] [pdf] String kernels and m

Senior Researcher Inria Paris, France Pehong Chen Distinguished Professor Emeritus Department of EECS Department of Statistics CLIMB Center Berkeley AI Research Lab Sky Computing University of California, Berkeley Email: jordan@cs.berkeley.edu Address: 387 Soda Hall #1776 University of California Berkeley, CA 94720-1776 Grants Administrator: Judy Tam jtam@erso.berkeley.edu Biographical highlights