Learning Bayesian Networks (part 1)

Page 1. 1 Learning Bayesian Networks (part 1) Mark Craven and David Page Computer Scices 760 Spring 2018 www.biostat.wisc.edu/~craven/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Tom Dietterich, Pedro Domingos, Tom Mitchell, David Page, and Jude Shavlik Goals for the lecture you should understand the following concepts • the Bayesian network representation • inference by enumeration • the parameter learning task for Bayes nets • the structure learning task for Bayes nets • maximum likelihood estimation • Laplace estimates • m-estimates • missing data in machine learning • hidden variables • missing at random • missing systematically • the EM approach to imputing missing values in Bayes net parameter learning Page 2. 2 Bayesian network example • Consider the following 5 binary random variables: B = a burglary occurs at your house …