Discrete-Time Expectation Maximization Algorithms for Markov-Modulated Poisson Processes
Date
2008
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Control Systems Society
Abstract
In this paper, we consider parameter estimation
Markov-modulated Poisson processes via robust filtering and
smoothing techniques. Using the expectation maximization algorithm
framework, our filters and smoothers can be applied to estimate
the parameters of ourmodel in either an online configuration
or an offline configuration. Further, our estimator dynamics do not
involve stochastic integrals and our new formulas, in terms of time
integrals, are easily discretized, and are written in numerically stable
forms inW. P.Malcolm, R. J. Elliott, and J. van der Hoek, “On
the numerical stability of time-discretized state estimation via clark
transformations,” presented at the IEEE Conf. Decision Control,
Mauii, HI, Dec. 2003.
Description
“© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Doi: 10.1109/TAC.2007.914305
Article deposited according to the policy found on the IEEE website, http://www.ieee.org/publications_standards/publications/rights/rights_policies.html, June 28, 2012.
Keywords
Change of measure, counting processes
Citation
R.J. Elliott and W. P. Malcolm. „Discrete Time Expectation Maximization Algorithms for Markov – Modulated Poisson Processes‟. IEEE Transactions on Automatic Control 53 (2008) 247-256