Bayesian Networks in R with Applications in Systems Biology /

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 wit...

Full description

Bibliographic Details
Main Authors: Nagarajan, Radhakrishnan. (Author), Scutari, Marco. (Author), Lb̈re, Sophie. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Series:Use R! ; 48
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6446-4
LEADER 02895nam a22004935i 4500
001 13584
003 DE-He213
005 20130727075045.0
007 cr nn 008mamaa
008 130427s2013 xxu| s |||| 0|eng d
020 # # |a 9781461464464  |9 978-1-4614-6446-4 
024 7 # |a 10.1007/978-1-4614-6446-4  |2 doi 
050 # 4 |a QA276-280 
072 # 7 |a UFM  |2 bicssc 
072 # 7 |a COM077000  |2 bisacsh 
082 0 4 |a 519.5  |2 23 
100 1 # |a Nagarajan, Radhakrishnan.  |e author. 
245 1 0 |a Bayesian Networks in R  |b with Applications in Systems Biology /  |c by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lb̈re.  |h [electronic resource] : 
264 # 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2013. 
300 # # |a XIII, 157 p. 36 illus.  |b online resource. 
336 # # |a text  |b txt  |2 rdacontent 
337 # # |a computer  |b c  |2 rdamedia 
338 # # |a online resource  |b cr  |2 rdacarrier 
347 # # |a text file  |b PDF  |2 rda 
490 1 # |a Use R! ;  |v 48 
505 0 # |a 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. 
520 # # |a 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. 
650 # 0 |a Statistics. 
650 # 0 |a Computer science. 
650 # 0 |a Mathematical statistics. 
650 1 4 |a Statistics. 
650 2 4 |a Statistics and Computing/Statistics Programs. 
650 2 4 |a Statistical Theory and Methods. 
650 2 4 |a Programming Languages, Compilers, Interpreters. 
700 1 # |a Scutari, Marco.  |e author. 
700 1 # |a Lb̈re, Sophie.  |e author. 
710 2 # |a SpringerLink (Online service) 
773 0 # |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781461464457 
830 # 0 |a Use R! ;  |v 48 
856 4 0 |u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6446-4 
912 # # |a ZDB-2-SMA 
950 # # |a Mathematics and Statistics (Springer-11649)