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 adap

A hands-on guide to Bayesian models with R, JAGS, Python, and Stan code, for a wide range of astronomical data types.

Comprehensive introduction to Bayesian methods in cosmological studies, for graduate students and researchers in cosmology, astrophysics and applied statistics.

This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These an

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest,