On the Viterbi process with continuous state space

Abstract

This paper deals with convergence of the maximum a posterior probability path estimator in hidden Markov models. We show that when the state space of the hidden process is continuous, the optimal path may stabilize in a way which is essentially different from the previously considered finite-state setting.

Published in at http://dx.doi.org/10.3150/10-BEJ294 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

References in corpus (3)

• The adjusted Viterbi training for hidden Markov models
• A complete solution to Blackwell's unique ergodicity problem for hidden Markov chains
• A constructive proof of the existence of Viterbi processes

Cited by in corpus (2)

• Regenerativity of Viterbi process for pairwise Markov models
• Online Target Localization using Adaptive Belief Propagation in the HMM Framework