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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Nagarajan, Radhakrishnan (Autor), Scutari, Marco (Autor), Lèbre, Sophie (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Use R!, 48
Temas:
Mathematical statistics-Data processing.
Statistics .
Compilers (Computer programs).
Statistics and Computing.
Statistical Theory and Methods.
Compilers and Interpreters.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Bayesian Networks in R  |h [electronic resource] :  |b with Applications in Systems Biology /  |c by Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre. 
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300 |a XIII, 157 p. 36 illus.  |b online resource. 
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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 Mathematical statistics-Data processing. 
650 0 |a Statistics . 
650 0 |a Compilers (Computer programs). 
650 1 4 |a Statistics and Computing. 
650 2 4 |a Statistical Theory and Methods. 
650 2 4 |a Compilers and Interpreters. 
700 1 |a Scutari, Marco.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Lèbre, Sophie.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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950 |a Mathematics and Statistics (R0) (SpringerNature-43713) 

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