Bayesian models for astrophysical data using R, JAGS, Python, and Stan
著者
書誌事項
Bayesian models for astrophysical data using R, JAGS, Python, and Stan
Cambridge University Press, 2017
- : hardback
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- 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.
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