Discrete-Time Expectation Maximization Algorithms for Markov-Modulated Poisson Processes
Otherexpectation maximization (EM) algorithm
SubjectChange of measure
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AbstractIn 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.
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