Bayesian Networks in R: with Applications in Systems Biology

Description Specifications
Description
Bayesian Networks in R: with Applications in Systems Biology (Use R!, 48)

Bayesian Networks in R with Applications in Systems Biology is unique as it 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 also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses 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 regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.
Specifications
Country USA
Brand Springer
Manufacturer Springer
Binding Paperback
ItemPartNumber 36 black & white illustrations, 15 black
ReleaseDate 2013-04-27
UnitCount 1
EANs 9781461464457

Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)

Statistical Analysis of Network Data with R (Use R!)

Graphical Models with R (Use R!)

Deep Learning (Adaptive Computation and Machine Learning series)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Python Machine Learning

Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

Bayesian Networks: With Examples in R (Chapman & Hall/CRC Texts in Statistical Science)

Statistical Analysis of Network Data with R (Use R!)

Graphical Models with R (Use R!)

Deep Learning (Adaptive Computation and Machine Learning series)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Python Machine Learning

Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

Country	USA
Brand	Springer
Manufacturer	Springer
Binding	Paperback
ItemPartNumber	36 black & white illustrations, 15 black
ReleaseDate	2013-04-27
UnitCount	1
EANs	9781461464457

Bayesian Networks in R: with Applications in Systems Biology (Use R!, 48)