Hybrid Algorithms For Maximum Likelihood And Maximum A Posterior Sequence Estimation

Abstract

This paper presents two methods of processing data generated by a hidden Markov model (HMM) such that the resulting state estimates are related to both the maximum likelihood (ML) estimates (generated by the Viterbi algorithm) and maximum a posreriori (MAP) estimates (generated by the HMM forwardbackward algorithm). Both algorithms contain a tuneable parameter which selects the tendency of the processing to replicate ML or MAP estimates. In the limit the algorithms reproduce the ML and MAP estimates exactly.