Bayesian Networks in R: with Applications in Systems Biology (Use R!)
|Rating||:||4.19 (823 Votes)|
|Number of Pages||:||157 Pages|
He earned his Ph.D. in Applied Mathematics at the University of Evry-val-d'Essone (France) under the guidance of Prof. Brogini, studying graphical model learning. in Statistics in Padova under the guidance of Prof. Dr. Prum. He is now Research Associate at the Genetics Institute, University College London (UCL). Scutari studied Statistics
“This book is a readable mix of short explanations of Bayesian network principles and implementations in R. 56 (3), August, 2014). … Each chapter has several exercises (answers are at the end of the book) and the book could be used as an introductory course text.” (Thomas Burr, Technometrics, Vol. I think it is most useful for readers who already have intermediate exposure to both the principles and R implementations
Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. 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 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. Bayesian networks have proven to be especially useful abstractions in this regard. 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.