The parameter’s MCMC estimation of HMMs with transition density function

DOI

10.2991/iccsee.2013.327How to use a DOI?

Keywords

HMM, Gibbs Sampling, Conjugate Priors

Abstract

The parameter estimation of HMM is critical to all its applications. The classic B-W algorithm is not flexible with the initial parameters and is easy to fall into the local optimal solution. Bayes estimation of it makes posterior risk minimization, and make full use of the experience, history information and other information other than samples, is useful in many cases. Employs the great computational power of MCMC, the MCMC estimation of HMM parameter can be more effective.

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  - Chengwen Zhu
AU  - Yu Ge
AU  - Lina Lu
AU  - Zhang Tian
AU  - Chuizhen Zeng
PY  - 2013/03
DA  - 2013/03
TI  - The parameter’s MCMC 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  - 1305
EP  - 1308
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.327
DO  - 10.2991/iccsee.2013.327
ID  - Zhu2013/03
ER  -