Data Analysis: A Bayesian Tutorial

 
9780198568322: Data Analysis: A Bayesian Tutorial

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'.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

About the Author:


Devinderjit Singh Sivia
Rutherford Appleton Laboratory
Chilton
Oxon
OX11 5DJ John Skilling
Maximum Entropy Data Consultants
42 Southgate Street
Bury St Edmonds
Suffolk
IP33 2AZ

Review:


"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


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Descrizione libro OUP Oxford 2006-06-01, Oxford, 2006. paperback. Condizione libro: New. Codice libro della libreria 9780198568322

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Descrizione libro Oxford University Press, United Kingdom, 2006. Paperback. Condizione libro: New. 2nd Revised edition. Language: English . Brand New Book. 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 . Features * An easy to read tutorial introduction to data anlaysis. * Concise, being one of the slimmest books in the field! * Self-contained--assumes little or no previous statistical training. * Good illustrative examples where the basic concepts are explained with a series of examples that become progressively more advanced, but that are always kept as simple as possible to aid understanding. * A contribution from John Skilling, an expert in numerical techniques. He introduces the simple but powerful new nested sampling technique for Bayesian computiaton. Codice libro della libreria AAU9780198568322

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Descrizione libro Oxford University Press, United Kingdom, 2006. Paperback. Condizione libro: New. 2nd Revised edition. Language: English . Brand New Book. 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 . Features * An easy to read tutorial introduction to data anlaysis. * Concise, being one of the slimmest books in the field! * Self-contained--assumes little or no previous statistical training. * Good illustrative examples where the basic concepts are explained with a series of examples that become progressively more advanced, but that are always kept as simple as possible to aid understanding. * A contribution from John Skilling, an expert in numerical techniques. He introduces the simple but powerful new nested sampling technique for Bayesian computiaton. Codice libro della libreria AAU9780198568322

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Descrizione libro Oxford University Press. Paperback. Condizione libro: new. BRAND NEW, Data Analysis: A Bayesian Tutorial (2nd Revised edition), Devinderjit Sivia, John Skilling, 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'. Features * An easy to read tutorial introduction to data anlaysis. * Concise, being one of the slimmest books in the field! * Self-contained--assumes little or no previous statistical training. * Good illustrative examples where the basic concepts are explained with a series of examples that become progressively more advanced, but that are always kept as simple as possible to aid understanding. * A contribution from John Skilling, an expert in numerical techniques. He introduces the simple but powerful new 'nested sampling' technique for Bayesian computiaton. Codice libro della libreria B9780198568322

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Descrizione libro Oxford University Press. Condizione libro: New. 2006. 2nd Edition. Paperback. Num Pages: 264 pages, 68 line drawings + 1 halftone. BIC Classification: PBT; PHS. Category: (P) Professional & Vocational. Dimension: 232 x 157 x 16. Weight in Grams: 416. . . . . . Books ship from the US and Ireland. Codice libro della libreria V9780198568322

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