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

被引:0
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
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
收藏
页码:448 / 451
页数:4
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