Modelling Discrete Input Probability Distributions
نویسنده
چکیده
Sometimes input probability distributions for stochastic models are not so simple that standard distributions suit. In this case, we model with weighted sums of standard distributions. These composed distributions may have many parameters which must be estimated. This is not easy with common estimation methods like maximum-likelihood. We use the genetic algorithm for that. The design of the composed distributions can be done such that the inverse tranformation method for the generation of random variates is feasible.
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