Bayesian Models for Astrophysical Data - Hilbe, Joseph M. (Jet Propulsion Laboratory, California Institute of Technology) a de Souza, Rafael S. (Eotvos Lorand University, Budapest) a Ishida, Emille E. O.

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. A must-have for astronomers, its concrete focus on modeling, analysis, and interpretation will also be attractive to researchers in the sciences more broadly.