Bayesian Models for Astrophysical Data

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. Rafael S. de Souza is a researcher at Eötvos Loránd University, Budapest. He is currently Vice-President for development of the International Astrostatistics Association (IAA) and was awarded the IAA's 2016 Outstanding Publication in Astrostatistics award. He has authored dozens of scientific papers, serving as the leading author for over twenty of them. Emille E. O. Ishida is a researcher at the Université Clermont-Auvergne (Université Blaise Pascal), France. She is cochair of the Cosmostatistics Initiative and coordinator of its Python-related projects. She is a specialist in machine learning applications to astronomy with special interests in type Ia supernovae spectral characterization, classification, and cosmology. She has been the lead author of numerous articles in prominent astrophysics journals and currently serves as chair of the IAA public relations committee.

• 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.

'This volume is a very welcome addition to the small but growing library of resources for advanced analysis of astronomical data. Astronomers are often confronted with complex constrained regression problems, situations that benefit from computationally intensive Bayesian approaches. The authors provide a unique and sophisticated guide with tutorials in methodology and software implementation. The worked examples are impressive. Many astronomers use Python and will benefit from the less familiar capabilities of R, Stan, and JAGS for Bayesian analysis. I suspect the work will also be useful to scientists in other fields who venture into the world of Bayesian computational statistics.' Eric D. Feigelson, Pennsylvania State University, author of Modern Statistical Methods for Astronomy