Bayesian Networks in R
with Applications in Systems Biology
Nagarajan, Radhakrishnan.
creator
author.
Scutari, Marco.
author.
Lèbre, Sophie.
author.
SpringerLink (Online service)
text
xxu
2013
monographic
eng
access
XIII, 157 p. 36 illus. online resource.
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.
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.
by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre.
Statistics
Programming languages (Electronic computers)
Statistics
Statistics and Computing/Statistics Programs
Statistical Theory and Methods
Programming Languages, Compilers, Interpreters
QA276-280
519.5
Springer eBooks
Use R! ; 48
9781461464464
http://dx.doi.org/10.1007/978-1-4614-6446-4
http://dx.doi.org/10.1007/978-1-4614-6446-4
130427
20160413122304.0
sulb-eb0022542