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Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support | 
 enlarge | Author: P. C. Gregory
Publisher: Cambridge University Press
Category: Book
List Price: $89.00
Buy New: $64.08
You Save: $24.92 (28%)
New (14) Used (8) from $54.95
Rating:  1 reviews
Sales Rank: 296006
Media: Hardcover
Pages: 486
Number Of Items: 1
Shipping Weight (lbs): 2.6
Dimensions (in): 9.8 x 6.7 x 1.1
ISBN: 052184150X
Dewey Decimal Number: 519.542
EAN: 9780521841504
Publication Date: May 23, 2005
Availability: Usually ships in 24 hours
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Product Description
Researchers in many branches of science are increasingly coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. It also discusses numerical techniques for implementing the Bayesian calculations, including Markov Chain Monte-Carlo integration and linear and nonlinear least-squares analysis seen from a Bayesian perspective.
Book Description
Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. The book also discusses numerical techniques for implementing the Bayesian calculations, including Markov Chain Monte-Carlo integration and linear and nonlinear least-squares analysis seen from a Bayesian pe rspective. Background material is provided in appendices and supporting Mathematica notebooks are available. Suitable for upper-undergraduates, graduate students, or any serious researcher in physical sciences or engineering.
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| Customer Reviews:
Excellent and practical Bayesian primer October 15, 2007
Vancouver Breton
1 out of 1 found this review helpful
Phil Gregory has managed to condense the most important aspects of Bayesian probability and data analysis into a book that is actually rather practical. This book will give you a solid Bayesian understanding of probability, starting with first principles (Cox's desiderata), continuing to Bayes theorem, an introduction to common probability distributions, and concluding with rather advanced numerical techniques such as tempered Markov Chain Monte Carlo. The book is geared towards a reader who will use Mathematica to work through examples, but can be successfully used by others who prefer cheaper and more practical computational frameworks. It's not a flawless book by any means---first of all, although the book purports to cover frequentist alternatives to Bayesian methods, the frequentist coverage is very shallow and inadequate to give the reader enough background to either use or really understand frequentist usage. The chapter on maximum entropy techniques is woefully incomplete, and doesn't include general Jeffreys priors (derived from the Fisher information) or really explain the various issues associated with defining the entropy for continuous distributions. The section on deriving priors with uncertain constraints actually doesn't give an answer to how to handle uncertain constraints! But on the plus side this book answered a number of questions that have long puzzled me, such as why frequentists marginalize over nuisance parameters by minimizing the likelihood function with respect to them instead of integrating over them (it's a dodge that only really works for Gaussian-like distributions), and how to handle the enormous numbers of parameters that Bayesian calculations can generate through the use of Markov Chain Monte Carlos. I wish the book had been longer or more detailed, but if you want to learn Bayesian analysis and don't care much about understanding frequentist statistics, this is an excellent place to start.
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