Improvement of Multidimensional Randomized Monte Carlo Algorithms with “Splitting”

Результат исследования: Научные публикации в периодических изданияхстатья

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 оригиналаанглийский
Страницы (с-по)775-781
Число страниц7
ЖурналComputational Mathematics and Mathematical Physics
Том59
Номер выпуска5
DOI
СостояниеОпубликовано - 1 мая 2019

Fingerprint Подробные сведения о темах исследования «Improvement of Multidimensional Randomized Monte Carlo Algorithms with “Splitting”». Вместе они формируют уникальный семантический отпечаток (fingerprint).

  • Цитировать