Backward Monte Carlo for probabilistic dynamics

Probabilistic dynamics studies the behavior of a system constituted of components and physical variables interacting together. Monte Carlo simulation is one way to solve the associated mathematical problem for a realistic system; however, Monte Carlo simulations are inefficient and biasing techniques are needed. Most research efforts have been aimed at the improvement of forward Monte Carlo schemes. A backward Monte Carlo simulation associated to the adjoint problem is studied in this paper. It can use any approximate forward solution to perform the importance biasing and can be exploited as a diagnostic approach. The method is illustrated on an example.