Estimating steady-state distributions via simulation-generated histograms
Computers and Operations Research
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This paper discusses implementation of a sequential procedure to estimate the steady-state density of a stochastic process. The procedure computes sample densities at certain points and uses Lagrange interpolation to estimate the density f (x). Even though the proposed sequential procedure is a heuristic, it does have strong basis. Our empirical results show that the procedure gives density estimates that satisfy a pre-specified precision requirement. An experimental performance evaluation demonstrates the validity of using the procedure to estimate densities.