SUBSPACE ESTIMATION AND PREDICTION METHODS FOR HIDDEN MARKOV MODELS

• 成果类型: Article
• 署名作者: Andersson, Sofia; Ryden, Tobias
• 署名单位: AstraZeneca; Lund University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/09-AOS711
• 发表日期: 2009
• 页码: 4131-4152
• 关键词: probabilistic functions

摘要

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 in-step linear predictor Computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear m-step predictor.