MLMM
MLMM, which stands for Multi-Level Monte Carlo Method, is a numerical technique used to efficiently estimate the expected value of complex functions, particularly in the context of stochastic processes and uncertainty quantification. It extends the traditional Monte Carlo approach by incorporating multiple levels of discretization or resolution, thereby reducing computational cost while maintaining accuracy.
The core principle of MLMM involves constructing a hierarchy of models with increasing fidelity. Coarse models
MLMM has applications across various fields, including financial mathematics, engineering, physics, and environmental modeling. It is
The method was formally introduced in the early 2000s and has since been developed with numerous variants
Overall, MLMM represents a significant advance in stochastic simulation techniques, enabling more feasible and scalable uncertainty