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Linear Optimal Prediction and Innovations Representations of Hidden Markov Models

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Author

Summary, in English

The topic of this paper is linear optimal prediction of hidden Markov models (HMMs) and innovations representations of HMMs. Our interest in these topics primarily arise from subspace estimation methods, which are intrinsically linked to such representations. For HMMs, derivation of innovations representations is complicated by non-minimality of the corresponding state space representations, and requires the solution of algebraic Riccati equations under non-minimality assumptions.

Publishing year

2003

Language

English

Pages

131-149

Publication/Series

Stochastic Processes and their Applications

Volume

108

Issue

1

Document type

Journal article

Publisher

Elsevier

Topic

  • Control Engineering
  • Probability Theory and Statistics

Keywords

  • Non-minimality
  • Kalman filter
  • Hidden Markov model
  • Innovations representation
  • Prediction error representation
  • Riccati equation

Status

Published

ISBN/ISSN/Other

  • ISSN: 1879-209X