IJPAM: Volume 112, No. 2 (2017)

Title

DEVELOPMENT OF THE MCR METHOD FOR ESTIMATION
OF PARAMETERS IN CONTINUOUS TIME
MARKOV CHAIN MODELS

Authors

Michele L. Joyner$^1$, Thomas Robacker$^2$
$^1$Department of Mathematics and Statistics
East Tennessee State University
Johnson City, USA
$^2$Department of Mathematics
Warren Wilson College
Asheville, USA

Abstract

Parameter estimation techniques have been successfully and extensively applied to deterministic models but are in early development for stochastic models. In this paper, we introduce a new method, the minimum cost realization method or MCR method, for approximating parameters for a continuous-time Markov chain (CTMC) model. This method is an adaption of well-established techniques used in parameter estimation for deterministic systems to account for the variability inherent in stochastic systems. Comparing this method to an established method, the MCR method provides significantly better estimates for parameter values on the two example models considered.

History

Received: November 30, 2016
Revised: December 6, 2016
Published: February 1, 2017

AMS Classification, Key Words

AMS Subject Classification: 60H35, 60J22, 60J27, 65C20, 45Q05, 49N45
Key Words and Phrases: MCR method, minimum cost realization, parameter estimation, Markov chain model, stochastic model, inverse problem

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How to Cite?

DOI: 10.12732/ijpam.v112i2.15 How to cite this paper?

Source: International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2017
Volume: 112
Issue: 2
Pages: 381 - 416


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