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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning

Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning

Chapter:
(p.30) Chapter 2 Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Christine Sinoquet

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0002

The aim of this chapter is to offer an advanced tutorial to scientists with no background or no deep background on probabilistic graphical models. To readers more familiar with these models, this chapter is to be used as a compendium of definitions and general methods, to browse through at will. Intentionally self-contained, this chapter first begins with reminders of essential definitions such as the distinction between marginal independence and conditional independence. Then the chapter briefly surveys the most popular classes of probabilistic graphical models: Markov chains, Bayesian networks, and Markov random fields. Next probabilistic inference is explained and illustrated in the Bayesian network context. Finally parameter and structure learning are presented.

Keywords:   probabilistic graphical models, Bayesian networks, Markov random fields, parameter learning, structure learning, probabilistic inference

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