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Cristopher M. Bishop's tutorial on graphical models

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Part 1: Graphical Models Machine Learning Techniques for Computer Vision Microsoft Research Cambridge ECCV 2004, Prague Christopher M. Bishop
About this Tutorial ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Probability Theory ,[object Object],[object Object],[object Object],[object Object]
Role of the Graphs ,[object Object],[object Object],[object Object],[object Object]
Decomposition ,[object Object],[object Object]
Directed Acyclic Graphs ,[object Object],No directed cycles
Undirected Graphs ,[object Object]
Conditioning on Evidence ,[object Object],[object Object]
Conditional Independences ,[object Object],[object Object]
“Explaining Away” ,[object Object],[object Object],image colour surface colour lighting colour
Directed versus Undirected
Example: State Space Models ,[object Object],[object Object]
Example: Bayesian SSM
Example: Factorial SSM ,[object Object],[object Object]
Example: Markov Random Field ,[object Object]
Example: Conditional Random Field
Inference ,[object Object]
Message Passing ,[object Object],[object Object],[object Object],[object Object],[object Object]
Message Passing ,[object Object],[object Object]
Message Passing ,[object Object],[object Object],[object Object]
Belief Propagation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Junction Tree Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Loopy Belief Propagation ,[object Object],[object Object],[object Object],[object Object]
Max-product Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inference and Learning ,[object Object],[object Object],[object Object],[object Object]
Regularized Maximum Likelihood ,[object Object],[object Object],[object Object],[object Object]
Bayesian Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Learning
Bayesian Learning
And Finally … the Exponential Family ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Illustration: the Gaussian ,[object Object],[object Object]
Maximum Likelihood ,[object Object],[object Object]
Conjugate Priors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary of Part 1 ,[object Object],[object Object],[object Object]

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Cristopher M. Bishop's tutorial on graphical models