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
Summary
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
Bibliography
Includes bibliographical references (pages 455-460) and index
