Regenerativity of Viterbi process for pairwise Markov models
This provides theoretical foundations for infinite Viterbi decoding in PMMs, which is incremental but relevant for researchers in statistical inference and hidden Markov models.
The paper proves that the joint process of the Viterbi process and pairwise Markov models (PMMs) is regenerative, using a construction of regeneration times based on barrier occurrences. As an application, it derives asymptotic results for the Viterbi training algorithm.
For hidden Markov models one of the most popular estimates of the hidden chain is the Viterbi path -- the path maximising the posterior probability. We consider a more general setting, called the pairwise Markov model (PMM), where the joint process consisting of finite-state hidden process and observation process is assumed to be a Markov chain. It has been recently proven that under some conditions the Viterbi path of the PMM can almost surely be extended to infinity, thereby defining the infinite Viterbi decoding of the observation sequence, called the Viterbi process. This was done by constructing a block of observations, called a barrier, which ensures that the Viterbi path goes trough a given state whenever this block occurs in the observation sequence. In this paper we prove that the joint process consisting of Viterbi process and PMM is regenerative. The proof involves a delicate construction of regeneration times which coincide with the occurrences of barriers. As one possible application of our theory, some results on the asymptotics of the Viterbi training algorithm are derived.