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

A hands-on guide to Bayesian models with R, JAGS, Python, and Stan code, for a wide range of astronomical data types.

Joseph M. Hilbe (Author), Rafael S. de Souza (Author), Emille E. O. Ishida (Author)

9781107133082, Cambridge University Press

Hardback, published 27 April 2017

408 pages, 66 b/w illus. 23 colour illus. 11 tables
25.3 x 19.3 x 2.4 cm, 1.1 kg

'… the focus of the book is not on providing a full understanding of how the distributions arise, but to give guidelines on how to write code for applications, including building multi-level models, and here it succeeds well, and is an excellent resource in conjunction with powerful packages such as STAN and JAGS.' Alan Heavens, The Observatory

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.

Preface
1. Astrostatistics
2. Prerequisites
3. Frequentist vs Bayesian methods
4. Normal linear models
5. GLM part I - continuous and binomial models
6. GLM part II - count models
7. GLM part III - zero-inflated and hurdle models
8. Hierarchical GLMMs
9. Model selection
10. Astronomical applications
11. The future of astrostatistics
Appendix A. Bayesian modeling using INLA
Bibliography
Index.

Subject Areas: Maths for engineers [TBJ], Astrophysics [PHVB], Theoretical & mathematical astronomy [PGC], Probability & statistics [PBT]