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  5. The explicit solution of inverse problem of continuous one-dimensional random variable modeling

The explicit solution of inverse problem of continuous one-dimensional random variable modeling

V.Yu. Dubnitskiy, Ir.G. Skorirova
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The explicit solution of inverse problem of continuous one-dimensional random variable modeling is defined. For its solution by known type of distribution it is necessary to find the explicit dependence of distribution parameters, which is modeling from set initial characteristics: ensemble average and standard deviation in the following cases: normal distribution, exponential distribution, Laplace distribution, extreme value minimum distribution, extreme value maximum distribution, double exponential distribution, logistic distribution, gamma distribution, Erlang distribution of n-th order, Rayleigh distribution, Maxwellian distribution, parabolic distribution, Simpson distribution, arc sine distribution, inverse Gaussian distribution , Cauchy distribution, one-parameter distribution of n-dimansional random value, hyperexponential distribution, beta distribution, common- beta distribution, Birnbaum-Sanders distribution.
Keywords: Monte Carlo method, statistical modeling, probability density function, n, inverse problem of statistic simulation, normal distribution, exponential distribution, Laplace distribution, minimum distribution, maximum distribution, double distribution, logistic distribution, gamma distribution, Erlang distribution of n-th order, Rayleigh distribution, Maxwellian distribution, parabolic distribution, Simpson distribution, inverse sine distribution, inverse Gaussian distribution, Cauchy distribution, one-parameter distribution of n-dimensional random value, hyperexponential distribution, beta distribution, common- beta distribution, Birnbaum-Sanders distribution