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Data Analysis : A Bayesian Tutorial by Devinderjit Sivia and John Skilling (2006, Trade Paperback)

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This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. Features An easy to read tutorial introduction to data anlaysis. He introduces the simple but powerful new 'nested sampling' technique for Bayesian computiaton.

About this product

Product Identifiers

PublisherOxford University Press, Incorporated

ISBN-100198568320

ISBN-139780198568322

eBay Product ID (ePID)52195617

Product Key Features

Number of Pages264 Pages

Publication NameData Analysis : a Bayesian Tutorial

LanguageEnglish

SubjectEngineering (General), General, Probability & Statistics / Bayesian Analysis

Publication Year2006

FeaturesRevised

TypeTextbook

Subject AreaMathematics, Technology & Engineering, Science

AuthorDevinderjit Sivia, John Skilling

FormatTrade Paperback

Dimensions

Item Height0.6 in

Item Weight14.4 Oz

Item Length9.2 in

Item Width6.3 in

Additional Product Features

Edition Number2

Intended AudienceScholarly & Professional

Reviews"Review from previous editionProviding a clear rationale for some of the most widely used procedures."--European Journal of Engineering Education "This small (less than 200 pages) but much-needed book contains a wealth of worked-out numerical examples of Bayesian treatments of data, expounded from a theoretical standpoint identical to ours. It should be considered an adjunct to the present work, supplying a great deal of practical advice for the beginner, at an elementary level that will be grasped readily by every science or engineering student."--Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003, "Review from previous edition Providing a clear rationale for some of the most widely used procedures."--European Journal of Engineering Education "This small (less than 200 pages) but much-needed book contains a wealth of worked-out numerical examples of Bayesian treatments of data, expounded from a theoretical standpoint identical to ours. It should be considered an adjunct to the present work, supplying a great deal of practical advice for the beginner, at an elementary level that will be grasped readily by every science or engineering student."--Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003, "Review from previous edition Providing a clear rationale for some of the most widely used procedures."--European Journal of Engineering Education"This small (less than 200 pages) but much-needed book contains a wealth of worked-out numerical examples of Bayesian treatments of data, expounded from a theoretical standpoint identical to ours. It should be considered an adjunct to the present work, supplying a great deal of practical advice for the beginner, at an elementary level that will be grasped readily by every science or engineering student."--Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003, 'Review from previous edition Providing a clear rationale for some of the most widely used procedures.'European Journal of Engineering Education'This small (less than 200 pages) but much-needed book contains a wealth of worked-out numerical examples of Bayesian treatments of data, expounded from a theoretical standpoint identical to ours. It should be considered an adjunct to the present work, supplying a great deal of practical advice for the beginner, at an elementary level that will be grasped readily by every science or engineering student.'Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003, One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples., 'Review from previous edition Providing a clear rationale for some of the most widely used procedures.'European Journal of Engineering Education, 'This small (less than 200 pages) but much-needed book contains a wealth of worked-out numerical examples of Bayesian treatments of data, expounded from a theoretical standpoint identical to ours. It should be considered an adjunct to the present work, supplying a great deal of practicaladvice for the beginner, at an elementary level that will be grasped readily by every science or engineering student.'Ed Jaynes in 'Probability Theory: The Logic of Science', CUP 2003

Dewey Edition22

IllustratedYes

Dewey Decimal830.9

Table Of Content1. The Basics2. Parameter Estimation I3. Parameter Estimation II4. Model Selection5. Assigning Probabilities6. Non-parametric Estimation7. Experimental Design8. Least-Squares Extensions9. Nested Sampling10. QuantificationAppendicesBibliography

Edition DescriptionRevised edition

SynopsisThis is the second edition of the first tutorial book on Bayesian methods and maximum entropy aimed at senior undergraduates in science and engineering. It takes the mystery out of statistics by showing how a few fundamental rules can be used to tackle a variety of problems in data analysis., Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design. The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'., Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.

LC Classification NumberQA279.5