Abstract
Randomized Monte Carlo algorithms are constructed by a combination of a basic probabilistic model and its random parameters to investigate parametric distributions of linear functionals. An optimization of the algorithms with a statistical kernel estimator for the probability density is presented. A randomized projection algorithm for estimating a nonlinear functional distribution is formulated and applied to the investigation of the criticality fluctuations of a particle multiplication process in a random medium.
| Original language | English |
|---|---|
| Pages (from-to) | 155-165 |
| Number of pages | 11 |
| Journal | Numerical Analysis and Applications |
| Volume | 12 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Apr 2019 |
Keywords
- double randomization method
- probabilistic model
- random medium
- random parameter
- randomized algorithm
- splitting method
- statistical kernel estimator
- statistical modeling