Bayesian Models for Astrophysical Data

Using R, JAGS, Python, and Stan

Nonfiction, Science & Nature, Mathematics, Statistics, Science
Cover of the book Bayesian Models for Astrophysical Data by Joseph M. Hilbe, Rafael S. de Souza, Emille E. O. Ishida, Cambridge University Press
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Author: Joseph M. Hilbe, Rafael S. de Souza, Emille E. O. Ishida ISBN: 9781108206693
Publisher: Cambridge University Press Publication: April 27, 2017
Imprint: Cambridge University Press Language: English
Author: Joseph M. Hilbe, Rafael S. de Souza, Emille E. O. Ishida
ISBN: 9781108206693
Publisher: Cambridge University Press
Publication: April 27, 2017
Imprint: Cambridge University Press
Language: English

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 to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and 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 interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and 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 interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

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