PDF Download Learning Probabilistic Graphical Models in R
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Learning Probabilistic Graphical Models in R
PDF Download Learning Probabilistic Graphical Models in R
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About the Author
David Bellot David Bellot is a PhD graduate in computer science from INRIA, France, with a focus on Bayesian machine learning. He was a postdoctoral fellow at the University of California, Berkeley, and worked for companies such as Intel, Orange, and Barclays Bank. He currently works in the financial industry, where he develops financial market prediction algorithms using machine learning. He is also a contributor to open source projects such as the Boost C++ library.
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Product details
Paperback: 250 pages
Publisher: Packt Publishing - ebooks Account (April 29, 2016)
Language: English
ISBN-10: 1784392057
ISBN-13: 978-1784392055
Product Dimensions:
7.5 x 0.6 x 9.2 inches
Shipping Weight: 1.1 pounds (View shipping rates and policies)
Average Customer Review:
2.9 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#1,268,312 in Books (See Top 100 in Books)
This is an excellent introduction to the theory of probabilistic graphical models and their implementation in R. One of the hallmarks of great writing is when the author anticipates questions the reader has and provides well thought out explanations. This is the case with Bellot's book. Highly recommended!
This is a December 2018 update - DO NOT BUY THIS OR ANY OTHER PACKT PUBLISHING BOOK UNLESS YOU CAN VERIFY THERE IS AN ERRATA FILE TO ACCOMPANY IT. PACKT PUBLISHING DOES NOT PRODUCE RELIABLE TEXTS. MY STUDENTS HAVE HAD A TERRIBLE TIME WITH PACKT BOOKS – THE COMPANY DOES RESPOND TO ANY QUESTIONS/REQUESTS - THE UNIVERSITY WHERE I TEACH IS NOW LOOKING CLOSELY AT THE VALIDITY OF THESE PUBLICATIONS.
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