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.

 

The book's concrete approach will also be attractive to researchers in the sciences more broadly.

J. M. Hilbe, R. S. de Souza and E. E. O. Ishida

Cambridge University Press, May/2017

In memory of

Joseph M. Hilbe (1944 - 2017)

Farewell Joe, it was an honor to meet you. 

This book is a product of our experiences within the Cosmostatistics Initiative (COIN), and an attempt to mitigate the barriers we encountered so many times in the exercise of constructing a truthfully interdisciplinary community. 

We hope it will be helpful to astronomers wishing to dig in the particularities of Bayesian modelling and to non-astronomers interested in applying their tools to astronomical data.

Online material is available at  this repository.

"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

Cover statements

"Encyclopedic in scope, a treasure trove of ready code for the hands-on practitioner"

 

Ben Wandelt

IAP - Lagrange Institute

Sorbonne University

"This informative book is a valuable resource for astronomers, astrophysicists and cosmologists at all levels of their career. From students starting out in the field to researchers at the frontiers of data analysis, everyone will find insightful techniques accompanied by helpful examples of code. With this book, Hilbe, de Souza and Ishida are firming taking astrostatistics into the 21st century."


Dr Roberto Trotta

Imperial College London

Laboratoire de Physique de Clermont - LPC

Universite Clermont Auvergne

Clermont-Ferrand, France

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