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CSE458 Bayesian Models: Lectures

Easter

There will be no practical classes, tutorials on the week starting April 5. Also, there will no lecture on Friday April 9.

Details of Lectures

Introduction

Overview of the course, introduction to decision analysis, decision flow diagrams, Bayes rule, averaging out and folding back.

More Decision Analysis

Uncertain payoffs, sampling costs, value of information, biased measurements, extensive form of decision analysis, strategies, and normal form of decision analysis.

Utility

Lotteries, utilities, utility curves, maximization of expected utility, risk aversion, risk control, and buying and selling prices.

Probability

Interpretation of probability, axioms of probability, independence, conditional probability, applications of Bayes' Rule, random variables, joint probabilities, and marginal distributions.

Bayesian Networks

Networks, semantics of Bayesian Networks, representation of joint probabilities, compactness, node ordering, and inference.

Decision Networks

Definition of a Decision Network, Semantics, and Decision Theory using Decision Networks.

Evaluation of Evaluating Decision Networks

Overview of different approaches to evaluate Decision Networks.

Dynamic Networks

Dynamic Bayesian Networks, Hidden Markov Models, Filtering, Prediction, Smoothing, most likely explanation, Dynamic Decision Networks, Markov Decision Processes.

Independence

Causal chains, common causes, common effects, blocking and independence.

Inference in Polytree Networks

Message passing, and polytree algorithm

Inference in Multiply Connected Networks

Overview of different approaches to inference in Multiply Connected Networks

Papers and other material of interest

Building Networks

Topics:Knowledge acquisition, constructing models, simplifying local models, discrete distributions, continuous distributions, model evaluation, and case studies.

Last modified 26/2/2004