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Inference in Graphical Models
Inference in Graphical Models
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Inference in Graphical Models
This short course will cover the basics of inference in graphical models. It will start by explaining the theory of probabilistic graphical models, including concepts of conditional independence and factorisation and how they arise in both Markov random fields and Bayesian Networks. He will then present the fundamental methods for performing exact probabilistic inference in such models, which include algorithms like variable elimination, belief propagation and Junction Trees. He will also briefly discuss some of the current methods for performing approximate inference when exact inference is not feasible. Finally, he will illustrate a range of real problems whose solutions can be formulated as inference in graphical models.
Channel: VideoLectures
Category: Educational
Video Length: 2960
Date Found: October 13, 2010
Date Produced: March 12, 2008
View Count: 1
 
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