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Inference in Bayesian Networks

Inference in Bayesian Networks. Pr: Evidence: Pr(e) Posterior marginals: Pr(x|e) for every X MPE: Most probable instantiation: Instantiation y such that Pr(y|e) is maximal (Y = E) MAP: Maximum a posteriori hypothesis: Intantiation y such that Pr(y|e) is maximal (Y is subset of E).

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Today

Today. What does it mean for a cipher to be: Computational secure? Unconditionally secure? Perfect secrecy Conditional probability Definition of perfect secrecy Systems that provide perfect secrecy How secure when we reuse a key? Entropy Redundancy of a language

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BAYESIAN NETWORKS CHAPTER#4

BAYESIAN NETWORKS CHAPTER#4. Book: Modeling and Reasoning with Bayesian Networks Author : Adnan Darwiche Publisher: CambridgeUniversity Press 2009. Introduction.

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Probability pr - PowerPoint PPT Presentation

Inference in Bayesian Networks

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BAYESIAN NETWORKS CHAPTER#4