Аннотация
Abstract: Randomized Monte Carlo algorithms are constructed by jointly realizing a baseline probabilistic model of the problem and its random parameters (random medium) in order to study a parametric distribution of linear functionals. This work relies on statistical kernel estimation of the multidimensional distribution density with a “homogeneous” kernel and on a splitting method, according to which a certain number n of baseline trajectories are modeled for each medium realization. The optimal value of n is estimated using a criterion for computational complexity formulated in this work. Analytical estimates of the corresponding computational efficiency are obtained with the help of rather complicated calculations.
Язык оригинала | английский |
---|---|
Страницы (с-по) | 775-781 |
Число страниц | 7 |
Журнал | Computational Mathematics and Mathematical Physics |
Том | 59 |
Номер выпуска | 5 |
DOI | |
Состояние | Опубликовано - 1 мая 2019 |