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Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks

By: Material type: TextTextPublication details: Morgan Kaufmann/Elsevier 2009 Amsterdam ; BostonDescription: xii, 406 pages : illustrations ; 25 cmISBN:
  • 9780123704764
Subject(s): DDC classification:
  • 572.80285 NEA-P
Contents:
Background. Probalistic informatics ; Probability basics ; Statistics basics ; Genetics basics -- Bayesian networks. Foundations of Bayesian networks ; Further properties of Bayesian networks ; Learning Bayesian network parameters ; Learning Bayesian network structure -- Bioinformatics applications. Nonmolecular evolutionary genetics ; Molecular evolutionary genetics ; Molecular phylogenetics ; Analyzing gene expression data ; Genetic linkage analysis.
Summary: The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.
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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 572.80285 NEA-P (Browse shelf(Opens below)) Available DCB1734

Background. Probalistic informatics ; Probability basics ; Statistics basics ; Genetics basics -- Bayesian networks. Foundations of Bayesian networks ; Further properties of Bayesian networks ; Learning Bayesian network parameters ; Learning Bayesian network structure -- Bioinformatics applications. Nonmolecular evolutionary genetics ; Molecular evolutionary genetics ; Molecular phylogenetics ; Analyzing gene expression data ; Genetic linkage analysis.

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.

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