Inference in Graphical Models
author:
Tibério Caetano,
National ICT Australia
Description
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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Part 1
0:49:20
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Part 2
1:08:00
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Part 3
0:34:12
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Part 4
0:58:03
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Part 5
0:55:44
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Part 6
1:14:05
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It's certainly a good introduction to the basics of GM, spends enough time to express the basic notions and is great specially for someone with little background on the topics. The downside might be that it's still a little "slow", even for a novice in this field.