Computational methodology in statistical inference II

FY2008, The Graduate University for Advanced Studies, ISM

Lecturer: Kenji Fukumizu (The Institute of Statistical Mathematics)

Schedule: Jan.7-Feb.25 Wednesday, 10:30-12:00

Place: Kensyu-shitsu

Purpose of course

An introduction to the method of graphical models, which represent probabilistic relations among variables using directed, undirected, or factor graphs. Methods of inference (belief propagation and related ones) and learning (parameter estimation), and approximation are discussed.

Questions for Report

To be given.

Plan of lectures

1. Introduction to Graphical Models. slides of part 1

2. Mixture Models and Hidden Markov Models slides of part 2

3. Inference with Graphical Models -- propagation algorithm slides 3-(a), slides 3-(b)

4. Learning of Graphical Models -- parameter estimation and structure learning slides 4-(a), slides 4-(b)

5. Approximate Inference slides 5



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