Linear Optimal Prediction and Innovations Representations of Hidden Markov Models
Andersson, Sofia ; Rydén, Tobias LU and Johansson, Rolf LU
(2003)
In Stochastic Processes and their Applications
108(1).
p.131-149
- Abstract
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/161696
- author
- Andersson, Sofia
; Rydén, Tobias
LU
and Johansson, Rolf
LU
- organization
- publishing date
- 2003
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Non-minimality, Kalman filter, Hidden Markov model, Innovations representation, Prediction error representation, Riccati equation
- in
- Stochastic Processes and their Applications
- volume
- 108
- issue
- 1
- pages
- 131 - 149
- publisher
- Elsevier
- external identifiers
-
- wos:000185803500006
- scopus:0141688288
- ISSN
- 1879-209X
- DOI
- 10.1016/S0304-4149(03)00086-3
- language
- English
- LU publication?
- yes
- id
- 6c093aa9-26f8-4750-a14a-7688dc2c86c6 (old id 161696)
- date added to LUP
- 2016-04-01 17:10:04
- date last changed
- 2022-04-07 21:20:28
@article{6c093aa9-26f8-4750-a14a-7688dc2c86c6,
abstract = {{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.}},
author = {{Andersson, Sofia and Rydén, Tobias and Johansson, Rolf}},
issn = {{1879-209X}},
keywords = {{Non-minimality; Kalman filter; Hidden Markov model; Innovations representation; Prediction error representation; Riccati equation}},
language = {{eng}},
number = {{1}},
pages = {{131--149}},
publisher = {{Elsevier}},
series = {{Stochastic Processes and their Applications}},
title = {{Linear Optimal Prediction and Innovations Representations of Hidden Markov Models}},
url = {{http://dx.doi.org/10.1016/S0304-4149(03)00086-3}},
doi = {{10.1016/S0304-4149(03)00086-3}},
volume = {{108}},
year = {{2003}},
}