FINITE-DIMENSIONAL MODELS FOR HIDDEN MARKOV-CHAINS

被引：5

作者：

AGGOUN, L ^{[1
]}

ELLIOTT, RJ ^{[1
]}

机构：

[1] UNIV ALBERTA,DEPT MATH SCI,EDMONTON,AB T6G 2G1,CANADA

来源：

ADVANCES IN APPLIED PROBABILITY | 1995年 / 27卷 / 01期

关键词：

RECURSIVE FILTER; UNNORMALIZED DENSITY; REFERENCE PROBABILITY; PARAMETER ESTIMATION; EM ALGORITHM;

D O I：

10.2307/1428101

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A continuous-time. non-linear filtering problem is considered in which both signal and observation processes are Markov chains. New finite-dimensional filters and smoothers are obtained for the state of the signal, for the number of jumps from one state to another, for the occupation time in any state of the signal, and for joint occupation times of the two processes. These estimates are then used in the expectation maximization algorithm to improve the parameters in the model. Consequently, our filters and model are adaptive, or self-tuning.

引用

页码：146 / 160

页数：15

共 15 条

[1] A MAXIMIZATION TECHNIQUE OCCURRING IN STATISTICAL ANALYSIS OF PROBABILISTIC FUNCTIONS OF MARKOV CHAINS [J].