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

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I have learned basics of graphical models from a professor who is quite prominent in the field.He taught it from unpublished book by Michael Jordan + few chapters by Chris Bishop.I have not read most of the books but have read enough to write positive things about it.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.

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.

This is a stunning, robust book on the theory of PGMs. If you want the maths, the theory, all the full glory, then this book is superb. If you are looking for a book about applications, how to code PGMs, how to build systems with these - then this book isn't it. It's a bit of a shame perhaps that it lacks explanations about how to apply these - but a great book non-the-less.

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!

A very good book completely spoilt by the Kindle Format. Here are my gripes:- Its directly taken from a PDF format and not adopted to work well in the Kindle Format.- The table of contents are not fine-grained enough when viewed from the side bar in kindle - so it is very hard to go to a section referenced in another section- The book is full of examples, figures equations which are referenced from far flung sections and there is no easy way to navigate to themFor such an expensive book, I would expect Amazon or the publisher to make good use of digital technology to make the reading experience easy. To follow a complex subject like this, being able to navigate between sections by clicking on a reference to an equation or an example is critical. I am really upset with Amazon as well as the Publishers. I find Amazon giving short shrift to a lot of technical books and so I am increasingly finding myself looking for alternative sources for ebooks. Reading this in plain PDF itself would have been so much easier.

Almost put together every pieces of theories about Bayesian network that I read from papers here and there. Well organized, clearly explained, most importantly, with human readable examples ,not only complex math formulas, which I hate for most of books. Explain most things that confused me for a long time, such as Dirichlet distribution. Except for one thing: REALLY LONG!

It's a great, authoritative book on the topic - no complains there. My one issue is that the shipped book is not colour but gray-scale print. I was hoping that's the least I could expect after paying over $100 on a book. Graphs and charts are imperative to reading technical books such as this, and anyone remotely familiar with ML/Statistics will agree with me that having coloured charts make an immense difference in this field.Is this book shipped from the actual publishers or a 3rd party vendor? Also, it'll be helpful to explicitly state the gray-scale print in description to manage expectations. Thanks.

Un testo notissimo e completo sull'argomento, senz'altro complesso ed esaustivo. Adatto all'autoapprendimento a partire da un buon livello di conoscenza.

This is an excellent but heavy going book on probabilistic graphic models. Covers most of the useful and interesting stuff in the field. But not much insight highlighted. You will need to find your gold in the book.relevant chapters in Pattern Recognition and Machine learning by Bishop might be an easier starter, and you might learn more insight by just reading through. Come back to this book as this has much more detailed treatment, but be warned, it is very dry.

It's a comprehensive book covering a diverse number of topics in probabilistic graphical models.The initial chapters discuss fundamental concepts which serves as background for the advanced chapters

This is the book of the (free MOOC) course "Probabilistic Graphical Models" of Coursera. Really good purchase. Totally recommended if you are interested in advanced statistics.