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

Includes bibliographical references and index.

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;
Appendices;
Index.

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.