Article
Licensed
Unlicensed
Requires Authentication
Mean-square optimization of Monte Carlo algorithms for estimation of the solution to an integral equation of the second kind
-
Published/Copyright:
August 10, 2010
Abstract
The mean-square error minimization of the global histogram-type error estimate of the Monte Carlo method is performed. In the special case, the simulation of a Markov chain with transitional density proportional to the product of the absolute value of the original kernel and the averaging weight is optimal. In the general case, the transitional density minimizing the weighted sum of the variances of several functionals is basic. A sufficiently simple approximation to the asymptotic variant of such density is obtained. The minimax variant of the algorithm is also considered.
Published Online: 2010-08-10
Published in Print: 2010-August
© de Gruyter 2010
You are currently not able to access this content.
You are currently not able to access this content.
Articles in the same Issue
- Numerical simulation of the structure of fully developed turbulent flow in a small-scale zone
- Unstable periodic orbits and sensitivity of the barotropic model of the atmosphere
- Mean-square optimization of Monte Carlo algorithms for estimation of the solution to an integral equation of the second kind
- A monotone nonlinear finite volume method for advection–diffusion equations on unstructured polyhedral meshes in 3D
- Special numerical models of discrete random series
- Tensor algorithms of blind separation of electromagnetic signals
Articles in the same Issue
- Numerical simulation of the structure of fully developed turbulent flow in a small-scale zone
- Unstable periodic orbits and sensitivity of the barotropic model of the atmosphere
- Mean-square optimization of Monte Carlo algorithms for estimation of the solution to an integral equation of the second kind
- A monotone nonlinear finite volume method for advection–diffusion equations on unstructured polyhedral meshes in 3D
- Special numerical models of discrete random series
- Tensor algorithms of blind separation of electromagnetic signals
