Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering

SISR based Hidden State Estimation of HMMs with transition density function

Authors
Longteng Li, Chengwen Zhu, Xiaoyan Cai, Chi Zhang, Chuizhen Zeng
Corresponding Author
Longteng Li
Available Online March 2013.
DOI
https://doi.org/10.2991/iccsee.2013.259How to use a DOI?
Keywords
HMM, MAP, SISR
Abstract
Traditional Viterbi algorithm cannot be generally effective. Regarding the hidden state estimates of HMM as a Bayes filtering problem, the Sequential Importance Sampling with Resampling algorithm could get an approximate of its Bayes estimates. Its performance reached or even exceeds the Viterbi algorithm while lower dependence on the model, having a wider range of adaptation.
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Proceedings
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccsee.2013.259How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Longteng Li
AU  - Chengwen Zhu
AU  - Xiaoyan Cai
AU  - Chi Zhang
AU  - Chuizhen Zeng
PY  - 2013/03
DA  - 2013/03
TI  - SISR based Hidden State Estimation of HMMs with transition density function
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.259
DO  - https://doi.org/10.2991/iccsee.2013.259
ID  - Li2013/03
ER  -