When models fail: an introduction to posterior predictive checks and model misspecification in gravitational-wave astronomy

Understand

Bayesian inference is a powerful tool in gravitational-wave astronomy.

• It enables us to deduce the properties of merging compact-object binaries and to determine how these mergers are distributed as a population according to mass, spin, and redshift.
• As key results are increasingly derived using Bayesian inference, there is increasing scrutiny on Bayesian methods.
• In this review, we discuss the phenomenon of \textit{model misspecification}, in which results obtained with Bayesian inference are misleading because of deficiencies in the assumed model(s).