Model Inference with Reference Priors

TL;DR

This paper demonstrates how reference priors can be used for model inference in high energy physics, enabling recursive Bayesian updates for complex parameter spaces.

Contribution

It introduces a method to map 1D reference posteriors to multi-dimensional parameter spaces and use them as priors for iterative inference.

Findings

Successfully applied to CKM matrix parameter estimation

Extended to parameters in a simplified SUSY model

Facilitates recursive Bayesian inference in high-dimensional spaces

Abstract

We describe the application of model inference based on reference priors to two concrete examples in high energy physics: the determination of the CKM matrix parameters rhobar and etabar and the determination of the parameters m_0 and m_1/2 in a simplified version of the CMSSM SUSY model. We show how a 1-dimensional reference posterior can be mapped to the n-dimensional (n-D) parameter space of the given class of models, under a minimal set of conditions on the n-D function. This reference-based function can be used as a prior for the next iteration of inference, using Bayes' theorem recursively.