# Data Analysis: A Bayesian Tutorial / Edition 2

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

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## Overview

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'.

## Product Details

ISBN-13:
9780198568322
Publisher:
Oxford University Press, USA
Publication date:
06/30/2006
Edition description:
REV
Pages:
264
Sales rank:
572,977
Product dimensions:
9.20(w) x 6.10(h) x 0.80(d)

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

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