Bayesian Models for Astrophysical Data

Erscheinungsjahr 2019
ISBN: 978-1-107-13308-2
Verlag: Cambridge University Press

Buch, Englisch, 424 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 1135 g

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 adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives, 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 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.

Autoren/Hrsg.

Hilbe, Joseph M.

De Souza, Rafael S.

Ishida, Emille E. O.

Fachgebiete

• Naturwissenschaften Astronomie Astrophysik
• Naturwissenschaften Physik Angewandte Physik Astrophysik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Naturwissenschaften Astronomie Astronomie: Allgemeines
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

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.

Über Autor(innen)

Ishida, Emille E. O.
Emille E. O. Ishida is a researcher at Université de Clermont-Ferrand II (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.

De Souza, Rafael S.
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.

Hilbe, Joseph M.
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.