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Probabilistic graphical models for genetics, genomics, and postgenomics /

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At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for model...

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Bibliographic Details
Other Authors: Sinoquet, Christine (Editor), Mourad, Raphaël (Editor)
Format: eBook
Language:English
Published: Oxford : Oxford University Press, 2014.
Subjects:
Genetics > Mathematical models.
Genomics > Statistical methods.
Genetics > Statistical methods.
Graphical modeling (Statistics)
Computational Biology > methods
Models, Genetic
Models, Statistical
Genomics > methods
Bayes Theorem
Computer Simulation
Génomique > Méthodes statistiques.
Génétique > Méthodes statistiques.
Modèles graphiques (Statistique)
Théorème de Bayes.
Simulation par ordinateur.
simulation.
SCIENCE > Life Sciences > Evolution.
Genetics > Mathematical models
Genetics > Statistical methods
Genomics > Statistical methods
Electronic books.
Online Access:Connect to the full text of this electronic book
Description
Summary:At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.
Physical Description:1 online resource (XXVII, 449 pages) : illustrations
Bibliography:Includes bibliographical references and index.
ISBN:9780191779619
019177961X
9780191019197
0191019194