Probabilistic Graphical Models - Caltech

More documents

Recommendations

Info

Announcements Project poster session on Thursday Dec 3, 4-6pm in Annenberg 2 nd floor atrium! Easels, poster boards and cookies will be provided! Final writeup (8 pages NIPS format) due Dec 9 2
Approximate inference Three major classes of general-purpose approaches Message passing E.g.: Loopy Belief Propagation (today!) Inference as optimization Approximate posterior distribution by simple distribution Mean field / structured mean field Assumed density filtering / expectation propagation Sampling based inference Importance sampling, particle filtering Gibbs sampling, MCMC Many other alternatives (often for special cases) 3
Page 1: Probabilistic Graphical Models Lect
Page 5 and 6: Monte Carlo sampling from a BN Sort
Page 7 and 8: Sampling from intractable distribut
Page 9 and 10: Designing Markov Chains 1) Proposal
Page 11 and 12: Summary of Sampling Randomized appr
Page 13 and 14: Assumed Recall: The “light” sid
Page 15 and 16: Remaining Challenges Inference Appr
Page 17 and 18: Dealing with missing data So far, h
Page 19 and 20: Learning with missing data Suppose
Page 21 and 22: E-Step: x: observed data; z: hidden
Page 23 and 24: Lower-bounding marginal likelihood
Page 25 and 26: M-step: Maximize lower bound Lower
Page 27 and 28: EM for Gaussian Mixtures 27
Page 29 and 30: EM in Bayes Nets Complete data like
Page 31 and 32: Need to compute E-Step for BNs For
Page 33 and 34: Computing expected counts Suppose w
Page 35 and 36: Structure learning with hidden data
Page 37: Learning general BNs Fully observab

Probabilistic Graphical Models - Caltech

Create successful ePaper yourself

Delete template?

Save as template?