Linear Optimal Prediction and Innovations Representations of Hidden Markov Models
Författare
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.
Publiceringsår
2003
Språk
Engelska
Sidor
131-149
Publikation/Tidskrift/Serie
Stochastic Processes and their Applications
Volym
108
Issue
1
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Control Engineering
- Probability Theory and Statistics
Nyckelord
- Non-minimality
- Kalman filter
- Hidden Markov model
- Innovations representation
- Prediction error representation
- Riccati equation
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 1879-209X