Making sense of your data: New Bayesian inference book by MPIA staff member
Science is fundamentally about learning from data, and doing so in the presence of uncertainty. In this new book, Coryn Bailer-Jones, a staff astronomer at the Max Planck Institute for Astronomy (MPIA), provides an introduction to the major concepts of probability and statistics and to the computational tools needed to analyse and interpret data. Bailer-Jones leads the Gaia group in MPIA's Galaxies and Cosmology department; the group is dedicated to extracting astrophysical information from the gigantic amounts of data collected by ESA's Gaia astrometry mission.
In his book, Bailer-Jones describes the Bayesian approach, explaining how it can be used to fit and compare models in a range of problems. Topics covered include regression, parameter estimation, model assessment, and Monte Carlo methods, as well as widely-used classical methods such as regularization and hypothesis testing. The emphasis throughout is on the principles, the unifying approach, and showing how the methods can be implemented in practice. R code (with explanations) is included, and available online, so readers can reproduce the plots and results for themselves, and go on to apply the techniques to a wide range of data analysis problems.
This book is aimed primarily at undergraduate and graduate science students. Knowledge of calculus is assumed, but no specific experience with probability or statistics is required. The book should also be useful for more experienced practitioners - in particular those with limited or no exposure to Bayesian methods - by providing an overview of the main concepts and techniques.
"Practical Bayesian Inference" is published by Cambridge University Press.