The parameters MCMC estimation of HMMs with transition density function

摘要：

　　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.

关键词： HMM Gibbs Sampling Conjugate Priors

作者: Chengwen Zhu Yu Ge Lina Lu Zhang Tian Chuizhen Zeng

作者单位: Wuhan Ordnancy Non-commissioned Officer Academy of PLA Wuhan,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 1306-1309

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）