Using history matching for prior choice

TL;DR

This paper introduces a novel numerical approach using history matching to select informative prior distributions in Bayesian models when prior information is limited and prior predictive distributions are analytically intractable.

Contribution

It develops a new application of history matching techniques for prior elicitation in complex Bayesian models with constrained prior information.

Findings

Effective methods for exploring prior choices under constraints

Application to logistic regression and high-dimensional models

Demonstrated usefulness in sparse signal shrinkage contexts

Abstract

It can be important in Bayesian analyses of complex models to construct informative prior distributions which reflect knowledge external to the data at hand. Nevertheless, how much prior information an analyst can elicit from an expert will be limited due to constraints of time, cost and other factors. This paper develops effective numerical methods for exploring reasonable choices of a prior distribution from a parametric class, when prior information is specified in the form of some limited constraints on prior predictive distributions, and where these prior predictive distributions are analytically intractable. The methods developed may be thought of as a novel application of the ideas of history matching, a technique developed in the literature on assessment of computer models. We illustrate the approach in the context of logistic regression and sparse signal shrinkage prior distributions for high-dimensional linear models.