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
- 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/).
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 (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 -