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Bayesian Networks
This document discusses Bayes networks for representing and reasoning about uncertainty. It begins by noting the benefits of using joint distributions to describe uncertain worlds but also the problem of using joint distributions due to their complexity. Bayes networks allow building joint distributions in manageable chunks by representing conditional independence relationships between variables. The document then discusses representing uncertainty using probability and key concepts in probability such as conditional probability, Bayes' rule, and working through examples to demonstrate their application.
