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I-maps and perfect maps

In this module, we describe Markov networks (also called Markov random fields): probabilistic graphical models based on an undirected graph representation. We discuss the representation of these models and their semantics. We also analyze the independence properties of distributions encoded by these graphs, and their relationship to the graph structure. We compare these independencies to those encoded by a Bayesian network, giving us some insight on which type of model is more suitable for which scenarios.

關於 Coursera

課程、專項課程和在線學位均由全世界一流大學和教育機構的頂尖授課教師教授。

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