Overfitting hidden Markov models with an unknown number of states

This paper presents new theory and methodology for the Bayesian estimation of overtted hidden Markov models, with nite state space. The goal is then to achieve posterior emptying of extra states. A prior conguration is constructed which favours congurations where the hidden Markov chain remains ergodic although it empties out some of the states. Asymptotic posterior con- vergence rates are proven theoretically, and demonstrated with a large sample simulation. The problem of overtted 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 et al. (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 asymp-