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Bayesian Models for Astrophysical Data
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Bayesian Models for Astrophysical Data : Using R, JAGS, Python, and Stan

by Emille E. O. Ishida , Joseph M. Hilbe , Rafael S. de Souza

Book Details

Format Hardback or Cased Book
ISBN-10 1107133084
ISBN-13 9781107133082
Publisher Cambridge University Press
Imprint Cambridge University Press
Country of Manufacture US
Country of Publication GB
Publication Date Apr 27th, 2017
Print length 408 Pages
Weight 1,102 grams
Dimensions 24.70 x 19.40 x 2.40 cms
Product Classification: Bayesian inference Theoretical & mathematical astronomy Astrophysics Maths for engineers
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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 to adapt. A must-have for astronomers, its concrete focus on modeling, analysis, and interpretation will also be attractive to researchers in the sciences more broadly.
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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