Optimization of Randomized Monte Carlo Algorithms for Solving Problems with Random Parameters

被引：1

作者：

Mikhailov, G. A. ^{[1
,2
]}

机构：

[1] Russian Acad Sci, Inst Computat Math & Math Geophys, Siberian Branch, Novosibirsk 630090, Russia

[2] Novosibirsk State Univ, Novosibirsk 630090, Russia

来源：

DOKLADY MATHEMATICS | 2018年 / 98卷 / 02期

基金：

俄罗斯基础研究基金会;

关键词：

PROBABILITY DENSITY; RADIATIVE-TRANSFER; MODELS;

D O I：

10.1134/S1064562418060157

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Randomized Monte Carlo algorithms intended for statistical kernel estimation of the averaged solution to a problem with random baseline parameters are optimized. For this purpose, a criterion for the complexity of a functional Monte Carlo estimate is formulated. The algorithms involve a splitting method in which, for each realization of the parameters, a certain number of trajectories of the corresponding baseline process are constructed.

引用

页码：448 / 451

页数：4

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