An EM algorithm for estimation in Markov-modulated Poisson processes
Computational Statistics & Data Analysis
A minimal representation of Markov arrival processes and a moments matching method
Performance Evaluation
Letter to the Editor: Computing multiple integrals involving matrix exponentials
Journal of Computational and Applied Mathematics
An EM algorithm for Markov modulated Markov processes
IEEE Transactions on Signal Processing
Transient features for Markovian binary trees
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
An EM algorithm for markovian arrival processes observed at discrete times
MMB&DFT'10 Proceedings of the 15th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
Extension of some MAP results to transient MAPs and Markovian binary trees
Performance Evaluation
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Markovian binary trees form a class of continuous-time branching processes where the lifetime and reproduction epochs of individuals are controlled by an underlying Markov process. An Expectation-Maximization (EM) algorithm is developed to estimate the parameters of the Markov process from the continuous observation of some populations, first with information about which individuals reproduce or die (the distinguishable case), and second without this information (the indistinguishable case). The performance of the EM algorithm is illustrated with some numerical examples. Fits resulting from the distinguishable case are shown not to be significantly better than fits resulting from the indistinguishable case using some goodness of fit measures.