Free Ebook Bayesian Networks in R: with Applications in Systems Biology (Use R!)
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Bayesian Networks in R: with Applications in Systems Biology (Use R!)
Free Ebook Bayesian Networks in R: with Applications in Systems Biology (Use R!)
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Review
“This book is a readable mix of short explanations of Bayesian network principles and implementations in R. I think it is most useful for readers who already have intermediate exposure to both the principles and R implementations. … 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. 56 (3), August, 2014)
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From the Back Cover
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.
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Product details
Series: Use R! (Book 48)
Paperback: 172 pages
Publisher: Springer; 2013 edition (April 27, 2013)
Language: English
ISBN-10: 1461464455
ISBN-13: 978-1461464457
Product Dimensions:
6.1 x 0.4 x 9.2 inches
Shipping Weight: 11.7 ounces (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#659,276 in Books (See Top 100 in Books)
Bayesian Networks in R focuses on the bnlearn package in R, and includes information about other Bayesian network packages such as catnet and deal. Table 2.1 shows the features of six different Bayesian network packages; this table alone makes the book valuable since it helps the researcher select the right tool for the job. More such tables comparing packages are desperately needed in the R community.I typed out every line of R code in the book. All of the code ran, except for the Rmpi package which would not load on my machines (this is not the authors' fault). The R language is always developing, so some of my output had more information than what the book shows. Functions that use Monte Carlo Markov chains gave slightly different values than the text (This is not a flaw; just know that you did not make a mistake when your numbers are close but not equal to the book).Two example data sets from Karen Sachs' research are on the book's website. When I loaded them, an X prefix was added to the column names. To get the examples to run, I added an X to each node's name. Everything ran smoothly after adding that.The examples are grouped by task, I feel that the book could be improved by grouping the examples by package instead. This would improve the book for referencing after a researcher selects a package for their task.
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