Bayesian Networks in R
Subtitle: with Applications in Systems Biology
By: by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre
Contributors: Scutari, Marco; Lèbre, Sophie; SpringerLink (Online service)
Series: Use R! -- 48
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] :
Number of pages: 157
ISBN-13: 9781461464464
Subjects: Mathematical statistics; Statistics; Computer science; Programming Languages, Compilers, Interpreters; Statistics and Computing/Statistics Programs; Statistical Theory and Methods
Description: 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.
Table of contents: title: Introduction; title: Bayesian Networks in the Absence of Temporal Information; title: Bayesian Networds in the Presence of Temporal Information; title: Bayesian Network Inference Algorithms; title: Parallel Computing for Bayesian Networks; title: Solutions; title: Index; title: References.