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Inference in Graphical Models

Inference in Graphical Models

This video was recorded at Machine Learning Summer School (MLSS), Kioloa 2008. 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.


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