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Data Analysis : A Bayesian Tutorial

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Description

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'.
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Product details

  • Paperback | 264 pages
  • 159 x 233 x 15mm | 408g
  • Oxford, United Kingdom
  • English
  • Revised
  • 2nd Revised edition
  • 68 line drawings + 1 halftone
  • 0198568320
  • 9780198568322
  • 213,692

Table of contents

1. The Basics ; 2. Parameter Estimation I ; 3. Parameter Estimation II ; 4. Model Selection ; 5. Assigning Probabilities ; 6. Non-parametric Estimation ; 7. Experimental Design ; 8. Least-Squares Extensions ; 9. Nested Sampling ; 10. Quantification ; Appendices ; Bibliography
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Review quote

One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. * Katie St. Clair MAA Reviews *
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About Devinderjit Sivia

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
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