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Bayesian Reasoning and Machine Learning 1st Edition

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David Barber (Author)
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David Barber (Author)
4.2 out of 5 stars 18 customer reviews
ISBN-13: 978-0521518147
ISBN-10: 0521518148
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Product Details

  • Hardcover: 735 pages
  • Publisher: Cambridge University Press; 1 edition (March 12, 2012)
  • Language: English
  • ISBN-10: 0521518148
  • ISBN-13: 978-0521518147
  • Product Dimensions: 7.4 x 1.5 x 9.7 inches
  • Shipping Weight: 3.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (18 customer reviews)
  • Amazon Best Sellers Rank: #327,558 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

5.0 out of 5 starsBrilliant and accessible
By T. Triche on May 17, 2012
Format: Hardcover
Don't take my word for it, though; read the book online. For some reason Amazon decided to delete the URL, so just do a search for David Barber and go to his home page at UCL (University College London), where links to a PDF of the book and to recent publications of his can be found.

Barber has done an excellent job of making extremely complex and contemporary ideas accessible to anyone with a reasonable mathematical background, and he puts them in context ("these techniques can be applied to finance, biology, and speech recognition"... para). Read through it and see for yourself. I find this book more accessible than Daphne Koller & Nir Friedman's (also excellent) text, Probabilistic Graphical Models, despite my immense respect for the authors of the latter.
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5.0 out of 5 starsExtremely Suitable for Self Study
By Let's Compare Options Preptorial TOP 500 REVIEWER on December 5, 2012
Format: Hardcover Verified Purchase
Unlike many (most?) books and courses on machine learning, Barber's outstanding text is very suitable for self study. There are many reasons for this, and high among them is the fact that he carefully explains, with commonsense examples and applications, many of the tougher logical, mathematical and processing foundations of pattern recognition.

For relative beginners, Bayesian techniques began in the 1700s to model how a degree of belief should be modified to account for new evidence. The techniques and formulas were largely discounted and ignored until the modern era of computing, pattern recognition and AI, now machine learning. The formula answers how the probabilities of two events are related when represented inversely, and more broadly, gives a precise mathematical model for the inference process itself (under uncertainty), where deductive reasoning and logic becomes a subset (under certainty, or when values can resolve to 0/1 or true/false, yes/no etc. In "odds" terms (useful in many fields including optimal expected utility functions in decision theory), posterior odds = prior odds * the Bayes Factor.

For context, I'm the lead scientist at IABOK dot org-- we design algorithms for huge data mining problems and applications. This text is our "go to" reference for programmers not up to speed in many of the new pattern recognition algorithms, including those writing new versions. All the most recent relevant models, from a probability standpoint, are represented here, with a clarity that is stunning.Read more ›
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5.0 out of 5 starsGreat effort put in explanations and examples
By Vladislavs Dovgalecs on June 13, 2012
Format: Hardcover
First I would like to thank authors (and the publishing house) for giving free PDF of the complete book. That's a really kind of them. Knowing its contents, that motivated me to buy a hard copy for my library.

Now to the content. The author has done a great job in introducing probabilistic concepts and pushing forward to more advanced and practically interesting techniques. There are many examples in the text that often help to grasp the workings of a method or an approach.

For me who has very little background in probabilistic methods this is a real textbook. I am still reading it chapter by chapter and can recommend it as a reading for advanced undergraduate, graduate and pHD students. The material in each chapter is well introduced and motivated, equations are just in time with variables and transformations explained, and with numerous exercises at the end of each chapter. All this makes a book almost self-consistent onto at least a semester can be taught.

The book is also accompanied with MATLAB code to which author refers to at the end of each chapter. The code is organized as a toolbox of functions with demos for each chapter. This allows to apply the acquired knowledge on your own problems.

As of the moment of writing, I've found just few typos that were not that disturbing. I don't see any other more serious reason not to give solid 5 stars to this book.
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5.0 out of 5 starsOne of the best text to learn machine learning
By quant@inside on September 26, 2012
Format: Hardcover Verified Purchase
I have read a similar book on Machine Learning, namely Pattern Recognition and Machine Learning (by Bishop). Before I read Barber's book, I considered Bishop's book to be the best in the Machine Learning (with bayesian focus). However, after reading this book, I can definitely say that it is better that Bishop's book in many sense. There are lots of examples in each chapter with matlab codes for many of them. Also, it covers more material that Bishop. Chapters on dynamic models and graphical models are particularly awesome. This book doesn't assume much background in probability (one master's level course on probability and statistics is probably more than enough). However, some chapters are advanced, and are mentioned so in the book.
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5.0 out of 5 starsA Truly Modern Introduction to Bayesian Reasoning and Machine Learning
By Adnan Masood, PhD on November 18, 2012
Format: Hardcover
If you are scouring for an exploratory text in probabilistic reasoning, basic graph concepts, belief networks, graphical models, statistics for machine learning, learning inference, naïve Bayes, Markov models and machine learning concepts, look no further. Barber has done a praiseworthy job in describing key concepts in probabilistic modeling and probabilistic aspects of machine learning. Don't let the size of this 700 page, 28 chapter long book intimidate you; it is surprisingly easy to follow and well formatted for the modern day reader.

With excellent follow ups in summary, code and exercises, Dr. David Barber a reader at University college London provides a thorough and contemporary primer in machine learning with Bayesian reasoning. Starting with probabilistic reasoning, author provides a refresher that the standard rules of probability are a consistent, logical way to reason with uncertainty. He proceeds to discuss the basic graph concepts and belief networks explaining how we can reason with certain or uncertain evidence using repeated application of Bayes' rule. Since belief network, a factorization of a distribution into conditional probabilities of variables dependent on parental variables, is a specific case of graphical models, the book leads us into the discipline of representing probability models graphically. Followed by efficient inference in trees and the junction tree, the text elucidates on key stages of moralization, triangularization, potential assignment, and message-passing.

I particularly enjoyed the follow up chapter called statistics for machine learning which uniquely discuss the classical univariate distributions including the exponential, Gamma, Beta, Gaussian and Poisson.Read more ›
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Most Recent Customer Reviews

5.0 out of 5 starsclassical textbook on Bayesian reasoning

detailed explanation on Bayesian reasoning from basic to advanced algorithm and design.

Published 10 months ago by Fei Shan
1.0 out of 5 starsThe content is wonderful but while I am reading

I have expected the publisher and the author made online modification to this 4th printing ... The content is wonderful but while I am reading , I do not want to consult errata... Read more

Published 10 months ago by mehdi
5.0 out of 5 starsFive Stars

Nice introduction to machine learning from the Bayesian perspective

Published 13 months ago by Amazon Customer
4.0 out of 5 starsFour Stars

seems to be a good guide for exploring financial models

Published 14 months ago by steve traendly
5.0 out of 5 starsWell written with the right material

This book is extremely well written. I have been reading it on my own, and the exercises are challenging without being so hard that I need a solution guide. Read more

Published 15 months ago by John Margaglione
5.0 out of 5 starsWide range of topics, good explanations with mathematical precision,...

BRML is one of the best machine learning books I've read (others include Bishops PRML, Alpaydin's book, and Marsland's algorithmic ML book). Read more

Published 17 months ago by Hadayat Seddiqi
4.0 out of 5 starsVery good introduction to Bayesian reasoning

Very good introduction to Bayesian reasoning. This book will be a good reference for anyone involved in use of Bayesian models.

Published on August 24, 2014 by Kay Johnny Steen
1.0 out of 5 starsUnreadable on Kindle

The equations are too small and do not resize with font.

Published on July 24, 2014 by Amazon Customer
4.0 out of 5 starsReally good book

Gives you formal definition as well several examples, however the book organization is not good. There are several examples or definitions which need definitions that has not been... Read more

Published on January 9, 2014 by Harold
5.0 out of 5 starsNice book with examples provided in appropriate places

I love the fact that motivating examples are provided. That really really really really helps. Some topics are not covered in depth, but it is always possible to read more online. Read more

Published on November 25, 2013 by Pavithra Kolar Srinath

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