Bayesian model selection for multilevel models using integrated likelihoods

TL;DR

This paper introduces a Bayesian approach for model selection in multilevel models by estimating integrated likelihoods, improving the accuracy and efficiency of model evidence computation in hierarchical data analysis.

Contribution

It presents a novel method for estimating log model evidence through intermediate marginalisation, reducing Monte Carlo sampling complexity in multilevel Bayesian models.

Findings

Method performs well on simulated data

Effective on real radon level dataset

Reduces computational complexity in model selection

Abstract

Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. Explicit expressions for these quantities are available for the simplest linear models with unrealistic priors, but in most cases, direct computation is impossible. In practice, Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform. We present a method for estimation of the log model evidence, by an intermediate marginalisation over non-variance parameters. This reduces the dimensionality of any Monte Carlo sampling algorithm, which in turn yields more consistent estimates. The aim of this paper is to show how this framework fits together and works in practice, particularly on data with hierarchical structure. We illustrate this method on simulated multilevel data and on a popular dataset containing levels of radon in homes in the US state of Minnesota.