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Bayesian Logical Data Analysis for the Physical

Sciences

A Comparative Approach with

Mathematica

Support

Increasingly, researchers in many branches of science are coming into contact with

Bayesian statistics or Bayesian probability theory. By encompassing both inductive

and deductive logic, Bayesian analysis can improve model parameter estimates by

many orders of magnitude. It provides a simple and unified approach to all data

analysis problems, allowing the experimenter to assign probabilities to competing

hypotheses of interest, on the basis of the current state of knowledge.

This book provides a clear exposition of the underlying concepts with large

numbers of worked examples and problem sets. The book also discusses numerical

techniques for implementing the Bayesian calculations, including an introduction

to Markov chain Monte Carlo integration and linear and nonlinear least-squares

analysis seen from a Bayesian perspective. In addition, background material is

provided in appendices and supporting

Mathematica

notebooks are available from

www.cambridge.org/052184150X, providing an easy learning route for upper-

undergraduate, graduate students, or any serious researcher in physical sciences

or engineering.

H I L

R E G O R Y

is Professor Emeritus at the Department of Physics and

Astronomy at the University of British Columbia.

Bayesian Logical Data Analysis for the Physical

Sciences

A Comparative Approach with

Mathematica

Support

P. C. Gregory

Department of Physics and Astronomy, University of British Columbia