Includes bibliographical references (pages 455-460) and index.
Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica notebooks are available.
Cover; Half-title; Title; Copyright; Contents; Preface; Acknowledgements; 1 Role of probability theory in science; 2 Probability theory as extended logic; 3 The how-to of Bayesian inference; 4 Assigning probabilities; 5 Frequentist statistical inference; 6 What is a statistic?; 7 Frequentist hypothesis testing; 8 Maximum entropy probabilities; 9 Bayesian inference with Gaussian errors; 10 Linear model fitting (Gaussian errors); 11 Nonlinear model fitting; 12 Markov chain Monte Carlo; 13 Bayesian revolution in spectral analysis; 14 Bayesian inference with Poisson sampling.