Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) 1st Edition

great book. must read for machine learning and a good reference book

If you want a very close look under the hood of Bayesian Networks, I can highly recommend Probabilistic Graphical Models. It's extremely comprehensive (1,200+ pages), well structured and clearly written. Theory, computation and application - including how to think about causation - are all covered in depth. Not light reading and not suited for those with limited stats background, but all in all one of the best textbooks on analytics topics I've ever read. Very impressive.

This is the textbook for my PGM class. It is definitely not an easy book to read, but its content is very comprehensive. It is a great reference to get more details of PGM. I highly recommend this book!

I have read this book in bits and pieces and find it extremely useful. Finally, we got a book that can be used in classroom settings. There are some typos (hence four stars) that will hopefully get fixed in the future editions. The book also has a lot of new insights to offer that can only be gleaned from the vast existing literature on the topic with excruciating labor. Agreed that this book is pricey but for what it has to offer, I think it was money well spent.

This is a great book on the topic, regardless of whether you are new to probabilistic graphical models or have some familiarity with them but would like a deeper exploration of theory and/or implementation. I have read a number of books and papers on this topic (including Barber's and Bishop's) and I much prefer this one. Dr. Koller's style of writing is to start with simple theory and examples and walk the reader up to the full theory, while adding reminders of relevant topics covered elsewhere. She accomplishes this without condescending to or belittling the reader, or being overly verbose; each of the 1200 pages is concise and well edited. There is an OpenClassroom course that accompanies the book (CS 228), which I highly recommend viewing, as it contains that same style of teaching but in a different format and often with a somewhat different approach.

I have learned basics of graphical models from a professor who is quite prominent in the field.He taught it from unpublished book by Michael Jordan + few chapters by Chris Bishop.I have not read most of the books but have read enough to write positive things about it. I especially like the part of the book that shows dependencies (bad pun alert). dependencies of chapters that is. :Dthe only complaint i have is not towards the authors but towards the publishers. the quality of paper is the worst i've ever seen and i own more than 400 textbooks. there are dusts all over the pages. you can feel your hands getting dry due to these paper particles and after a while you can't breathe because of these particles. some books have this but this book is the worst when it comes to that paper dust. you will know when you have this yourself.they could have slapped on $200 and worse paper quality, I would still buy it without thinking twice about it.

This popular book makes a noble attempt at unifying the many different types of probabilistic models used in artificial intelligence. It seems like a good reference manual for people who are already familiar with the fundamental concepts of commonly used probabilistic graphical models. However, it contains a lot of rambling and jumping between concepts that will quickly confuse a reader who is not already familiar with the subject. While the book appears to be systematic in introducing the subject with mathematical rigor (definitions and theorems), it actually skips a lot of fundamental concepts and leaves a lot of important proofs as exercises. I would recommend that a beginner in the subject start with another book like that by Jordan and Bishop, while keeping this book around as a reference manual or bank of practice problems for further study. The Coursera class on this subject is much easier to follow than this book is.

with an eye to taking the course. Very informative. Although the phrase "in context" covers a multitude of sins. I'd prefer the distinction between the the distribution of an intersection of random variables (where comma's are used as a short-hand) and joint distributions a bit clearer.Aside, I managed to find an error not listed on the errata web page for the book. The equation for MAP queries on page 26 has it as the maximal assignment of a JOINT distribution, while on the next page it is the maximal assignment of a CONDITIONAL distribution (I believe this is the correct one). This was a little confusing until I read page 26 a bit closer.Before you ask, yes I do read Math textbooks for pleasure.