Probabilistic Graphical Models: Principles and Techniques Author: Daphne Koller | Language: English | ISBN:
B007CNRD62 | Format: EPUB
Probabilistic Graphical Models: Principles and Techniques Description
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
- File Size: 141590 KB
- Print Length: 1280 pages
- Publisher: MIT Press; 1 edition (November 19, 2009)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B007CNRD62
- Text-to-Speech: Not enabled
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- Lending: Enabled
- Amazon Best Sellers Rank: #135,307 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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- #17
in Books > Computers & Technology > Software > Natural Language Processing - #20
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Engineering > Mechanical > Robotics - #28
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Engineering > Computer Technology > Robotics & Automation
Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.
By Dr. Kasumu Salawu
If you're trying to learn probabilistic graphical models on your own, this is the best book you can buy.
The introduction to fundamental probabilistic concepts is better than most probability books out there and the rest of the book has the same quality and in-depth approach. References, discussions and examples are all chosen so that you can take this book as the centre of your learning and make a jump to more detailed treatment of any topic using other resources.
Another huge plus is Professor Daphne Koller's online course material. Her course for probabilistic models is available online, and watching the videos alongside the book really helps sometimes.
If you have a strong mathematical background, you may find the book a little bit too pedagogic for your taste, but if you're looking for a single resource to learn the topic on your own, then this book is what you need.
The only problem with it is that it is a big book to carry around, and if you buy the Kindle edition for the iPad, you'll have to zoom into pages to read comfortably(or maybe I have bad eye sight), and Kindle app on iPad does not keep the zoom level across pages. So my experience is, zoom, pan, read, change page, zoom, pan, go back to previous page to see something, zoom, pan... You get the idea. I'd gladly pay more for a pdf version which I could read with other software on the iPad. Even though my reading experience has been a bit unpleasant due to Kindle app, the book deserves five stars, since it is the content that matters.
By S. Arikan
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