Joseph M. Hilbe

Bayesian Models for Astrophysical Data (eBook, PDF)

Using R, JAGS, Python, and Stan

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 27. April 2017
• Englisch
• ISBN-13: 9781108216142
• Artikelnr.: 48204065

Autorenporträt

Joseph M. Hilbe is Solar System Ambassador with NASA's Jet Propulsion Laboratory, California Institute of Technology, Adjunct Professor of Statistics at Arizona State University, and Professor Emeritus at the University of Hawaii. He is currently President of the International Astrostatistics Association (IAA) and was awarded the IAA's 2016 Outstanding Contributions to Astrostatistics medal, the association's top award. Hilbe is an elected Fellow of both the American Statistical Association and the IAA and is a full member of the American Astronomical Society. He has authored nineteen books on statistical modeling, including leading texts on modeling count and binomial data. His book, Modeling Count Data (Cambridge, 2014) received the 2015 PROSE honorable mention for books in mathematics.

Inhaltsangabe

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.