Bayesian inference provides a simple and unified approach to data
analysis, allowing experimenters to assign probabilities to
competing hypotheses of interest, on the basis of the current state
of knowledge. By incorporating relevant prior information, it can
sometimes improve model parameter estimates by many orders of
magnitude. This book provides a clear exposition of the underlying
concepts with many worked examples and problem sets. It also
discusses implementation, including an introduction to Markov chain
Monte-Carlo integration and linear and nonlinear model fitting.
Particularly extensive coverage of spectral analysis (detecting and
measuring periodic signals) includes a self-contained introduction
to Fourier and discrete Fourier methods. There is a chapter devoted
to Bayesian inference with Poisson sampling, and three chapters on
frequentist methods help to bridge the gap between the frequentist
and Bayesian approaches. Supporting Mathematica (R) notebooks with
solutions to selected problems, additional worked examples, and a
Mathematica tutorial are available at
www.cambridge.org/9780521150125.
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