Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Extremely well written. Best resource for learning message-passing algorithms, specially for causal relations or directed acyclic graphs. I bought it for 20$, very cheap for such a cool book. It was soft cover though. In one word, Excellent!!!

Recently I needed to learn the principles of Bayesian networks quickly, so I bought three books: this one by Pearl, "Pattern Recognition and Machine Learning" by Bishop, and "Bayesian Artificial Intelligence" by Korb and Nicholson. Each has a very different audience and different set of strengths. The Bishop book would probably be a great text for a serious student with a year to spend learning the theory of machine learning. But I found it a bit too concise, with a bias towards an "algebraic" description rather than a "geometric" one (my preference). I wound up spending a lot of time trying to translate equations into mental pictures in order to grasp the concepts. Too much work, so I dropped this after a couple of days. Next I tackled the Korb and Nicholson book. This one's aimed at the application engineer who wants to get a network up and running quickly, and is not too concerned about how it works. I've been in that position many times in my career, and have always welcomed books like this for giving me a quick start into a new field. But this time I needed to really understand how Bayesian networks worked, and for this the Korb and Nicholson book is not great. In the first 9 pages of chapter 3 they try to explain the belief propagation algorithm, but their hearts just weren't it in--I found their explanation to be unintelligible. (I suspect most readers just skim this to get to the applications.) So after several days of struggling and getting nowhere, I tossed this aside as well. The Pearl book was the only left; I put it off to last since I was initially somewhat intimidated by it. After all, this is one of the books that kicked off the "Bayesian revolution," so I was fearing a foundational math book consisting of one dry theorem after another. Not so! Although you have to read 174 pages to get through the belief propagation algorithm for trees, this took far less time than reading the first 62 pages of Korb and Nicholson, which cover roughly the same ground. The reason: Pearl is a gifted teacher and writer. His explanations are a series of baby steps, leaving nothing out, never assuming the reader will make "obvious" inferences, and supplying motivation every step of the way. Although he doesn't have a lot of figures in the book, the ones he does have are excellent, and by the time I hit page 175, I had a clear picture in my head of not just how it all worked, but why it worked. In fact, after just two days of reading, I was able to implement the belief propagation algorithm in software in an afternoon (I tested the software with examples from the Korb and Nicholson book, so that book was ultimately useful). Pearl made the subject seem almost obvious. If you are looking for a book to help you get canned Bayesian software up and running for an application quickly, this is not it. But if you want to really understand how these things work, and don't have a lot of time available, I cannot imagine a better book than this.

Pearl's book is a classic in artificial intelligence. Many of his ideas are still being studied today.

This book is an absolutely essential book for AI programming. I've found no better book for explaining the recent advances in probability theory and its relevance to real-life, practical artificial intelligence development. It's written in a very down-to-earth and highly entertaining style with plenty of examples.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.

One of the best references on probability theory and uncertain reasoning, this book is one of my most prized. It's lucid enough to be an excellent textbook for the novice, and thorough enough to be a valuable reference for the experienced. It's a book that will always remind me (lest I forget) of the importance of probabilistic reasoning in AI.