Validation of Bayesian posterior distributions using a multidimensional Kolmogorov--Smirnov test

TL;DR

This paper introduces a multidimensional Kolmogorov--Smirnov test that validates Bayesian posterior distributions by mapping them to a one-dimensional space, enabling sensitive assessment of inference assumptions across various applications.

Contribution

The authors develop a general multidimensional K-S test using a probability content mapping, allowing validation of Bayesian posteriors of any dimension, surpassing traditional software validation methods.

Findings

Successfully applied to a 2D Gaussian toy problem.

Validated Bayesian inference in astrophysical galaxy cluster analysis.

Demonstrated sensitivity to inference assumptions.

Abstract

We extend the Kolmogorov--Smirnov (K-S) test to multiple dimensions by suggesting a $\mathbb{R}^n \rightarrow [0,1]$ mapping based on the probability content of the highest probability density region of the reference distribution under consideration; this mapping reduces the problem back to the one-dimensional case to which the standard K-S test may be applied. The universal character of this mapping also allows us to introduce a simple, yet general, method for the validation of Bayesian posterior distributions of any dimensionality. This new approach goes beyond validating software implementations; it provides a sensitive test for all assumptions, explicit or implicit, that underlie the inference. In particular, the method assesses whether the inferred posterior distribution is a truthful representation of the actual constraints on the model parameters. We illustrate our multidimensional K-S test by applying it to a simple two-dimensional Gaussian toy problem, and demonstrate our method for posterior validation in the real-world astrophysical application of estimating the physical parameters of galaxy clusters parameters from their Sunyaev--Zel'dovich effect in microwave background data. In the latter example, we show that the method can validate the entire Bayesian inference process across a varied population of objects for which the derived posteriors are different in each case.