CSI 661 - Uncertainty in A.I. Lecture 9 1

CSI 661 - Uncertainty in A.I. Lecture 9 2

CSI 661 - Uncertainty in A.I. Lecture 9 3

Three Main Approaches To Approximate Inference

• MCMC

• Variational Methods

• Loopy belief propagation

CSI 661 - Uncertainty in A.I. Lecture 9 4

Decision Making With BNets

CSI 661 - Uncertainty in A.I. Lecture 9 5

Minimize Risk

CSI 661 - Uncertainty in A.I. Lecture 9 6

Influence Diagrams

CSI 661 - Uncertainty in A.I. Lecture 9 7

Putting a Price on Information

CSI 661 - Uncertainty in A.I. Lecture 9 8

Improvement With Extra Link

CSI 661 - Uncertainty in A.I. Lecture 9 9

Dynamic Belief Networks

CSI 661 - Uncertainty in A.I. Lecture 9 10

DBNet

CSI 661 - Uncertainty in A.I. Lecture 9 11

HMM Algorithms Applied to DBNET

CSI 661 - Uncertainty in A.I. Lecture 9 12

Next Lecture

• Major criticism of BNets as models of human behavior

• Learning Bayes Nets– Parameter estimation– Learning structure