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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… Click to show full abstract
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.
Keywords:
optimization randomized;
monte;
monte carlo;
randomized monte;
carlo algorithms
Journal Title: Doklady Mathematics
Year Published: 2018
Link to full text (if available)
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Journal Title: Doklady Mathematics
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!