Lecture 19-Probabilistic Reasoning

# Lecture 19-Probabilistic Reasoning - CS 561: Artificial...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, macskass@usc.edu TAs: Nadeesha Ranashinghe ( nadeeshr@usc.edu ) William Yeoh ( wyeoh@usc.edu ) Harris Chiu ( chiciu@usc.edu ) Lectures: MW 5:00-6:20pm, OHE 122 / DEN Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Spring2010/ This class will use http://www.uscden.net/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (2nd ed)

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CS561 - Lecture 19 - Macskassy - Spring 2010 2 Probabilistic Reasoning [Ch. 14] Bayes Networks Part 1 Syntax Semantics Parameterized distributions Inference Part2 Exact inference by enumeration Exact inference by variable elimination Approximate inference by stochastic simulation Approximate inference by Markov chain Monte Carlo
CS561 - Lecture 19 - Macskassy - Spring 2010 3 Bayesian networks A simple, graphical notation for conditional independence assertions and hence for compact specification of full joint distributions Syntax: a set of nodes, one per variable a directed, acyclic graph (link ¼ “directly influences”) a conditional distribution for each node given its parents: P ( X i | Parents ( X i )) In the simplest case, conditional distribution represented as a conditional probability table (CPT) giving the distribution over X i for each combination of parent values

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CS561 - Lecture 19 - Macskassy - Spring 2010 4 Example Topology of network encodes conditional independence assertions: Weather is independent of the other variables Toothache and Catch are conditionally independent, given Cavity
CS561 - Lecture 19 - Macskassy - Spring 2010 5 Example I'm at work, neighbor John calls to say my alarm is ringing, but neighbor Mary doesn't call. Sometimes it's set off by minor earthquakes. Is there a burglar? Variables: Burglar , Earthquake , Alarm , JohnCalls , MaryCalls Network topology reflects “causal” knowledge: A burglar can set the alarm off An earthquake can set the alarm off The alarm can cause Mary to call The alarm can cause John to call

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6 Example contd. Probabilities derived
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## Lecture 19-Probabilistic Reasoning - CS 561: Artificial...

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