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Bayesian Models for Astrophysical Data

Using R, JAGS, Python, and Stan

Bayesian Models for Astrophysical Data by Joseph M. Hilbe
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This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives, then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretation to address scientific questions. A must-have for astronomers, the book's concrete approach will also be attractive to researchers in the sciences more broadly.
Cambridge University Press; April 2017
ISBN 9781108206693
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Title: Bayesian Models for Astrophysical Data
Author: Joseph M. Hilbe; Rafael S. de Souza; Emille E. O. Ishida