Optimization of Monte Carlo weighted methods for part of variables
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I. N. Medvedev
We consider problems whose mathematical model is associated with a certain Markov chain, in this case it is necessary to estimate the linear functionals of the solution to the corresponding integral equation of the second kind. Auxiliary variables whose values define transitions in the initial chain are included in the number of coordinates of the phase space to construct the weight modifications of numerical statistical simulation. Auxiliary weight after the realization of each auxiliary random variable is multiplied by the ratio of the corresponding densities of the initial distribution to the modelled one. In this paper, we solve the minimization problem of the variances of the estimates for linear functionals by varying the modelled distribution density of one (the first in order) auxiliary, maybe vector, random variable.
Copyright 2003, Walter de Gruyter
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Articles in the same Issue
- Numerical aspects of one stabilization method
- Numerical aspects of one stabilization method
- Investigation of the convergence rate of the multigrid method on quasinested adaptive grids
- Investigation of the convergence rate of the multigrid method on quasinested adaptive grids
- Numerical solution of quasilinear parabolic equations and backward stochastic differential equations
- Numerical solution of quasilinear parabolic equations and backward stochastic differential equations
- Optimization of Monte Carlo weighted methods for part of variables
- Optimization of Monte Carlo weighted methods for part of variables
- Grid approximation of a singularly perturbed one-dimensional heat equation on an unbounded domain excluding a rectangle with sides noncollinear to the axes
- Grid approximation of a singularly perturbed one-dimensional heat equation on an unbounded domain excluding a rectangle with sides noncollinear to the axes
