2014/04/30 by Diana Harrison, D. L. Harrison, Harrison, Diana +8 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Bayesian Methods and Mixture Models #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistical Methods and Bayesian Inference #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.1404.7735
16 pages, 11 figures, accepted by MNRAS
openalex publication_date 2014/04/30 · arxiv created 2015/05/15 · arxiv updated 2015/05/18 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
We extend the Kolmogorov--Smirnov (K-S) test to multiple dimensions by suggesting a ℝn → [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.