Subspace estimation and prediction methods for hidden Markov models

Details Subspace estimation and prediction methods for hidden Markov models Subspace estimation and prediction methods for hidden Markov models

2009-07-27

arxiv.org/abs/0907.4418v2

Annals of Statistics 2009, Vol. 37, No. 6B, 4131-4152

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/09-AOS711 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/09-AOS711

Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subspace estimation methods for HMMs whose output lies a finite set as well. In particular, we study the geometric structure arising from the nonminimality of the linear state space representation of HMMs, and consistency of a subspace algorithm arising from a certain factorization of the singular value decomposition of the estimated linear prediction matrix. For this algorithm, we show that the estimates of the transition and emission probability matrices are consistent up to a similarity transformation, and that the $m$-step linear predictor computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear $m$-step predictor.

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