CS 561, Session 29 1 Belief networks Conditional independence Syntax and semantics Exact inference Approximate inference.

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Presentation transcript:

CS 561, Session 29 1 Belief networks Conditional independence Syntax and semantics Exact inference Approximate inference

CS 561, Session 29 2 Independence

CS 561, Session 29 3 Conditional independence

CS 561, Session 29 4 Conditional independence

CS 561, Session 29 5 Conditional independence

CS 561, Session 29 6 Belief networks

CS 561, Session 29 7 Example

CS 561, Session 29 8 Semantics

CS 561, Session 29 9 Semantics

CS 561, Session Markov blanket

CS 561, Session Constructing belief networks

CS 561, Session Example

CS 561, Session 29 13

CS 561, Session 29 14

CS 561, Session 29 15

CS 561, Session 29 16

CS 561, Session Example: car diagnosis

CS 561, Session Example: car insurance

CS 561, Session Compact conditional distributions

CS 561, Session Compact conditional distributions

CS 561, Session Hybrid (discrete+continuous) networks

CS 561, Session Continuous child variables

CS 561, Session Continuous child variables

CS 561, Session Discrete variable w/ continuous parents

CS 561, Session Discrete variable

CS 561, Session Inference in belief networks Exact inference by enumeration Exact inference by variable elimination Approximate inference by stochastic simulation Approximate inference by Markov chain Monte Carlo (MCMC)