Bayesian models for astrophysical data using R, JAGS, Python, and Stan

Bayesian models for astrophysical data using R, JAGS, Python, and Stan

by Hilbe, Joseph M.

Type Book
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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. 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 i…

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Title Statements

  • Full title :Bayesian models for astrophysical data using R, JAGS, Python, and Stan / Joseph M. Hilbe, Rafael S. De Souza, Emille E.O. Ishida.

Summary

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. -- Back cover.

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Edition Statements

  • Publication Date:©2017

Publication Statements

The Physical Object

  • Description:xvii, 393 pages, 18 unnumbered pages of plates : : illustrations (some color) ; : 26 cm

Edition Identifiers

  • ISBN:9781107133082, 1107133084
  • oclc:ocn975270186
  • LC Classification:QB149
  • Dewey Decimal:520.1/519542

Table of Contents

Astrostatistics -- Prerequisites -- Frequent vs. Bayesian methods -- Normal linear models -- GLMs Part 1: Continuous and binomial models -- GLMs Part 2: Count models -- GLMs Part 3: Zero-inflated and hurdle models -- Hierarchical GLMMs -- Model selection -- Astronomical applications -- The future of astrostatistics.

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Subjects

Astronomy -- Data processing statistical astronomy Statistical astronomy -- Data processing Statistique stellaire Statistique stellaire -- Informatique

Writers

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

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