## Abstract

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.

Original language | English |
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Pages (from-to) | 775-781 |

Number of pages | 7 |

Journal | Computational Mathematics and Mathematical Physics |

Volume | 59 |

Issue number | 5 |

DOIs | |

Publication status | Published - 1 May 2019 |

## Keywords

- complexity of functional estimate
- double randomization method
- Monte Carlo method
- probabilistic model
- random medium
- randomized algorithm
- splitting method
- statistical kernel estimate
- statistical modeling
- MODELS