Overfitting hidden Markov models with an unknown number of states

TL;DR

This paper develops Bayesian methods for overfitted hidden Markov models, ensuring extra states are emptied out through specific priors, with theoretical guarantees and practical MCMC solutions demonstrated via simulations.

Contribution

It introduces new prior configurations and extends prior parallel tempering for Bayesian estimation of overfitted HMMs, with proven asymptotic convergence and practical implementation.

Findings

Asymptotic posterior convergence rates are established.

Different priors are compared through simulation studies.

Extended MCMC methods improve estimation of complex posterior spaces.

Abstract

This paper presents new theory and methodology for the Bayesian estimation of overfitted hidden Markov models, with finite state space. The goal is then to achieve posterior emptying of extra states. A prior configuration is constructed which favours configurations where the hidden Markov chain remains ergodic although it empties out some of the states. Asymptotic posterior convergence rates are proven theoretically, and demonstrated with a large sample simulation. The problem of overfitted HMMs is then considered in the context of smaller sample sizes, and due to computational and mixing issues two alternative prior structures are studied, one commonly used in practice, and a mixture of the two priors. The Prior Parallel Tempering approach of van Havre (2015) is also extended to HMMs to allow MCMC estimation of the complex posterior space. A replicate simulation study and an in-depth exploration is performed to compare the three priors with hyperparameters chosen according to the asymptotic constraints alongside less informative alternatives.