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in this video were gonna talk about a different family of graphical models called undirected graphical models so well start by defining what I mean by an undirected graphical model and Ill give you a the more formal term for the the types that were gonna look at which is something called a Markov random field and we will discuss then what the independence of some assumptions are that are encoded in Markov random fields then finally well talk about the relationship between Markov random fields and Bayesian networks that weve seen previously in fact well ask about ask ourselves whether Bayesian networks are just instances of MRFs so as a quick review this is the slide I showed you a couple of videos ago about Bayesian networks and in a Bayesian network you have nodes representing variables and edges representing conditional probability tables between variables and the rule was that you have a conditional probability table for every single node that that is conditioned on its pare
