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Cover

Data Analysis

A Bayesian Tutorial

Second Edition

Devinderjit Sivia and John Skilling

01 June 2006

ISBN: 9780198568322

264 pages
Paperback
234x156mm

Price: £35.49

This 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.

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Description

This 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.

  • 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 computaton.

New to this edition

  • Completely updated graduate text.
  • Three new chapters, two of which are from new co-author, John Skilling.

About the Author(s)

Devinderjit Sivia, Rutherford Appleton Laboratory and St Catherine's College, Oxford, and John Skilling, Maximum Entropy Data Consultants

Table of Contents

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

Reviews

"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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