Cristopher M. Bishop's tutorial on graphical models

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

Example: State Space Models ,[object Object],[object Object]

Example: Factorial SSM ,[object Object],[object Object]

Example: Markov Random Field ,[object Object]

Example: Conditional Random Field

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]

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]

Cristopher M. Bishop's tutorial on graphical models

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