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Bayesian Networks in R [electronic resource] : with Applications in Systems Biology / by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre.

By: Nagarajan, Radhakrishnan [author.].
Contributor(s): Scutari, Marco [author.] | Lèbre, Sophie [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Use R!: 48Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XIII, 157 p. 36 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461464464.Subject(s): Statistics | Computer science | Mathematical statistics | Statistics | Statistics and Computing/Statistics Programs | Statistical Theory and Methods | Programming Languages, Compilers, InterpretersDDC classification: 519.5 Online resources: Click here to access online
Contents:
Introduction -- Bayesian Networks in the Absence of Temporal Information -- Bayesian Networds in the Presence of Temporal Information -- Bayesian Network Inference Algorithms -- Parallel Computing for Bayesian Networks -- Solutions -- Index -- References.
In: Springer eBooksSummary: Bayesian Networks in R with Applications in Systems Biology introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is gradually increased across the chapters with exercises and solutions for enhanced understanding and hands-on experimentation of key concepts. Applications focus on systems biology with emphasis on modeling pathways and signaling mechanisms from high throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regards as exemplified by their ability to discover new associations while validating known ones. It is also expected that the prevalence of publicly available high-throughput biological and healthcare data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.
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Introduction -- Bayesian Networks in the Absence of Temporal Information -- Bayesian Networds in the Presence of Temporal Information -- Bayesian Network Inference Algorithms -- Parallel Computing for Bayesian Networks -- Solutions -- Index -- References.

Bayesian Networks in R with Applications in Systems Biology introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is gradually increased across the chapters with exercises and solutions for enhanced understanding and hands-on experimentation of key concepts. Applications focus on systems biology with emphasis on modeling pathways and signaling mechanisms from high throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regards as exemplified by their ability to discover new associations while validating known ones. It is also expected that the prevalence of publicly available high-throughput biological and healthcare data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.

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