Overfitting Bayesian Mixture Models with an Unknown Number of Components

TL;DR

This paper introduces Zmix and Zswitch, innovative methods for accurately estimating the number of components in overfitted Bayesian mixture models, addressing non-identifiability, sampling limitations, and label switching.

Contribution

The paper presents Zmix and Zswitch, novel algorithms that improve estimation accuracy and computational efficiency in overfitted Bayesian mixture models with unknown components.

Findings

Zmix accurately estimates the number of mixture components.

Zswitch effectively resolves label switching with low computational cost.

Methods are validated through simulations and real case studies.

Abstract

This paper proposes solutions to three issues pertaining to the estimation of finite mixture models with an unknown number of components: the non-identifiability induced by overfitting the number of components, the mixing limitations of standard Markov Chain Monte Carlo (MCMC) sampling techniques, and the related label switching problem. An overfitting approach is used to estimate the number of components in a finite mixture model via a Zmix algorithm. Zmix provides a bridge between multidimensional samplers and test based estimation methods, whereby priors are chosen to encourage extra groups to have weights approaching zero. MCMC sampling is made possible by the implementation of prior parallel tempering, an extension of parallel tempering. Zmix can accurately estimate the number of components, posterior parameter estimates and allocation probabilities given a sufficiently large sample size. The results will reflect uncertainty in the final model and will report the range of possible candidate models and their respective estimated probabilities from a single run. Label switching is resolved with a computationally light-weight method, Zswitch, developed for overfitted mixtures by exploiting the intuitiveness of allocation-based relabelling algorithms and the precision of label-invariant loss functions. Four simulation studies are included to illustrate Zmix and Zswitch, as well as three case studies from the literature. All methods are available as part of the R package Zmix, which can currently be applied to univariate Gaussian mixture models