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Bayesian Logical Data Analysis for the Physical
Sciences
parative Approach with MathematicaTM Support
Increasingly, researchers in many branches of science ing into contact with
Bayesian statistics or Bayesian probability theory. By passing 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 peting
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
184150X, providing an easy learning route for upper-
undergraduate, graduate students, or any serious researcher in physical sciences
or engineering.
P HIL G REGORY is Professor Emeritus at the Department of Physics and
Astronomy at the University of British Columbia.
Bayesian Logical Data Analysis for the Physical
Sciences
parative Approach with MathematicaTM Support
P. C. Gregory
Department of Physics and Astronomy, University of British Columbia
Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo
Cambridge University Press
The Edinburgh Building, Cambridge ,UK
Published in the United States of America by Cambridge University Press, New York
Information on this title: /9780521841504
© Cambridge University Press 2005
This book is in copyright. Subject to statutory exception and to the provision of
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