Abstract
A numerical technique for the identification of discrete-state continuoustime Markov models is presented. The technique is characterized by utilizing both initial approximations derived from observed data and estimates of minimized criterion sensitivity to small variations of identified parameters. A new feature of the proposed approach is improved running time of the applied combinatorial optimization algorithm achieved through replacing, at each iteration, of enumeration of various combinations of parameter values in the neighborhood of their current estimates by enumeration of values of only those parameters to which the minimized criterion is highly sensitive.
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