SISR based Hidden State Estimation of HMMs with transition density function

DOI

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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 (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1032
EP  - 1035
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.259
DO  - 10.2991/iccsee.2013.259
ID  - Li2013/03
ER  -