- Save 10% on Books for Schools offered by Amazon.co.uk when you purchase 10 or more of the same book. Here's how (terms and conditions apply) Enter code SCHOOLS2016 at checkout. Here's how (terms and conditions apply)
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning) Paperback – 1 Sep 1988
|New from||Used from|
- Choose from over 13,000 locations across the UK
- Prime members get unlimited deliveries at no additional cost
- Find your preferred location and add it to your address book
- Dispatch to this address when you check out
Special Offers and Product Promotions
Frequently Bought Together
Customers Who Bought This Item Also Bought
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
About the Author
By Judea Pearl
What Other Items Do Customers Buy After Viewing This Item?
Top Customer Reviews
Most Helpful Customer Reviews on Amazon.com (beta)
This book has revolutionized the field of AI, and made Bayesian networks ubiquitous in computer science today (though, BNs were first proposed in 1970 by Suppes or perhaps even earlier).
[ Interestingly, Suppes used BNs to deal with causality. ]
I think part of this book's originality is the use of a mathematical theory (ie, probability theory) into AI. A similar and earlier revolutionary step was taken by John McCarthy in his use of formal logic in AI.
Chapter 5 is actually about what I'd call probabilistic abduction, but the naming of the chapter is a bit misleading.
There are now newer and perhaps better texts on BNs, eg: "Learning BNs" by Neapolitan, the tome by Koller and Friedman (MIT Press), and Darwiche
I've been looking for a good introduction to Bayes nets for a long time, and this one is by far the best and most comprehensive.
Probability is increasingly becoming one of the major foundations of effective artificial intelligence, and I strongly recommend this book to anyone with an interest in AI or probability theory.
"Probabilistic Reasoning in Intelligent Systems" provides very comprehensive and detailed discussion on topics like why uncertainty is important, probabilistic reasoning for query answering system, Markov and Bayesian networks etc; It goes beyond the text and into philosophical discussion as well, for instance it talks about what Bayesian rule's mathematical representation actually mean. The topic "Learning structures from data" is a good discussion of belief networks. As an advance text book, it's equipped with theorem proofs, exercises but not very many examples which disappoints. The book covers default logic very well; topics like semantics for default reasoning, casualty modularity and tree structures, evidential reasoning in taxonomic hierarchies, decision analysis, and autonomous propagation as a computational paradigm are some of the well discussed ones. I particularly enjoyed the Bayesian vs. Dempster-Shafer formulism, probabilistic treatment of the Yale shooting problem and dialogue between logicist and probablist, the concluding discussion.
I'd recommend this book as a secondary resource for advance researchers in the field of probability and uncertainty.
Look for similar items by category
- Books > Computing & Internet > Computer Science > Artificial Intelligence
- Books > Computing & Internet > Digital Lifestyle > Online Shopping > Amazon
- Books > Computing & Internet > Networking & Security > Network Topics
- Books > Computing & Internet > Programming > Algorithms
- Books > Science & Nature > Engineering & Technology > Education > Higher Education
- Books > Science & Nature > Engineering & Technology > Electronics & Communications Engineering > Telecommunications
- Books > Scientific, Technical & Medical > Engineering > Electronics & Telecommunications Engineering
- Books > Society, Politics & Philosophy > Philosophy