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I'd give the first chapter or two of this book 5 stars. It's very well written, and the material is exceptionally well motivated. Pearl gives the "why" in a lot of places where others give only the "how" or the "what." That being said, the book shows its age in places. Apparently Daphne Koller and some others are writing a book which aims to replace this one, and which treats modern subjects such as iterated/generalized belief propagation and sampling more fully. Until then, this is a great plac...
probability is not really about numbers, it is about the structure of reasoning -Glen ShaferBy no means an introductory book; even chapter 1 will mean little to you if you haven't tried to model situations with both formal logic and probabilities before. (Some set theory wouldn't go amiss either.) Parts of it treat nearly-irrelevant dead controversies, just because he was still fighting off the McCarthy / production systems programme in the late Eighties. (For instance, I learned Dempster-Sh
I have a lot to learn about probabilistic reasoning.
If you read Judea Pearl's "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference" then you will see that the basic insight behind graphical models is indispensable to problems that require it. (It's not something that fits on a T-Shirt, I'm afraid, so you'll have to go and read the book yourself. I haven't seen any online popularizations of Bayesian networks that adequately convey the reasons behind the principles, or the importance of the math being exactly the way it
The approach is scientific and interesting especially concluding the book on discussions between a logicist and probabilistic! I learnt a lot especially the art of incising an event, the process and think it out in all means of probabilistic reasoning, applying in my field of VUCA research. Thx Judea
Great book on a topic that Dr. Pearl can rightly claim he pioneered the most useful approach for.
I got this book as a reference on the use of probability and statistical distributions in modelling. I found it useful, but beyond that I was impressed by the clarity of the writing, the useful examples, and the discussions around the use of the various techniques.The book is intended to describe the author's work on the applicability of probabilistic methods to AI that relies on automated reasoning under uncertainty. Many of the techniques are based on Bayesian inference. There is a chapter on
Good thorough examples and clear writing put this book ahead of most textbooks' treatment of related subjects. Despite its early publication date, it is very forward-thinking, even in terms of computational paradigms, so that it seemed perhaps even more relevant today than at the time of writing.
