On forward recursive estimation for bivariate Markov chains

Author

Ephraim, Yariv ; Mark, Brian L.

Author_Institution

Dept. of ECE, George Mason Univ., Fairfax, VA, USA

fYear

2012

fDate

21-23 March 2012

Firstpage

1

Lastpage

6

Abstract

A bivariate Markov chain comprises a pair of finite-alphabet continuous-time random processes, which are jointly, but not necessarily individually, Markov. Forward recursive conditional mean estimators are developed for the state, the number of jumps from one state to another, and the total sojourn time of the process in each state. The recursions are implemented using Clark´s transformation and tested in estimating the parameter of the bivariate Markov chain using the expectation-maximization (EM) algorithm.¹

Keywords

Markov processes; expectation-maximisation algorithm; random processes; recursive estimation; Clark transformation; bivariate Markov chain; expectation-maximization algorithm; finite-alphabet continuous-time random processes; forward recursive conditional mean estimators; forward recursive estimation; parameter estimation; total sojourn time; Differential equations; Generators; Markov processes; Maximum likelihood estimation; Noise measurement; Vectors; Markov chain; Zakai equation; recursive estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Sciences and Systems (CISS), 2012 46th Annual Conference on

Conference_Location

Princeton, NJ

Print_ISBN

978-1-4673-3139-5

Electronic_ISBN

978-1-4673-3138-8

Type

conf

DOI

10.1109/CISS.2012.6310833

Filename

6310833