# Robustly estimating the marginal likelihood for cognitive models via importance sampling

## TL;DR

This paper introduces an efficient importance sampling method for unbiasedly estimating the marginal likelihood in Bayesian models with intractable likelihoods, facilitating model comparison in cognitive science.

## Contribution

It proposes a novel importance sampling approach that provides unbiased marginal likelihood estimates using samples from MCMC, with robustness and computational efficiency.

## Key findings

- Method successfully estimates marginal likelihood in hierarchical cognitive models.
- Approach is robust to sampling quality and target distribution.
- Code implementation is freely available for further research.

## Abstract

Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments allow us to estimate model parameters, other basic problems such as estimating the marginal likelihood, a fundamental tool in Bayesian model selection, remain challenging. This is an important scientific limitation because testing psychological hypotheses with hierarchical models has proven difficult with current model selection methods. We propose an efficient method for estimating the marginal likelihood for models where the likelihood is intractable, but can be estimated unbiasedly. It is based on first running a sampling method such as MCMC to obtain samples for the model parameters, and then using these samples to construct the proposal density in an importance sampling (IS) framework with an unbiased estimate of the likelihood. Our method has several attractive properties: it generates an unbiased estimate of the marginal likelihood, it is robust to the quality and target of the sampling method used to form the IS proposals, and it is computationally cheap to estimate the variance of the marginal likelihood estimator. We also obtain the convergence properties of the method and provide guidelines on maximizing computational efficiency. The method is illustrated in two challenging cases involving hierarchical models: identifying the form of individual differences in an applied choice scenario, and evaluating the best parameterization of a cognitive model in a speeded decision making context. Freely available code to implement the methods is provided. Extensions to posterior moment estimation and parallelization are also discussed.