Monash University > School of Computer Science and Software Engineering > CSE458> Lectures

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.

(ps) (pdf)

More Decision Analysis

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

(ps) (pdf)

Utility

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

(ps) (pdf)

Probability

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

(ps) (pdf)

Bayesian Networks

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

(ps) (pdf)

Alarm Network

Aids Network

Urn Network

Stud Farm Network

Decision Networks

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

(ps) (pdf)

Alarm Network (Bayesian Network)

Alarm Network (Correct Decsion Network)

Alarm Network (Incorrect Decision Network)

Urn Network for e0

Urn Network for e1

Urn Network for e2

Incorrect Urn Network for es

Urn Network for es

Total Urn Network (version 1)

Total Urn Network (version 2)

Total Urn Network (version 3)

Evaluation of Evaluating Decision Networks

Overview of different approaches to evaluate Decision Networks.

(ps) (pdf)

Dynamic Networks

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

(ps) (pdf)

Independence

Semantics of Bayesian Networks, Construction of Bayesian Networks, Makov Blanket, Blocking, Independence, and Inference in Dynamic Bayesian Networks.

(ps) (pdf)

Inference in Polytree Networks

Backward-chaining and Message passing

(ps) (pdf) (ppt)

Backward-chaining algorithm (ps)

Inference in Multiply Connected Networks

Overview of different approaches to inference in Multiply Connected Networks

(ps) (pdf) (ppt)

A-Formula Network

B-Formula Network

Wet Grass Network

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.

(ps) (pdf)

Last modified 11/6/2004