A combined importance splitting and sampling algorithm for rare event estimation

We propose some methodological basis for an improvement to the splitting method for a Markov process that evolves over a deterministic time horizon. Our algorithm is based on a decomposition of the selection functions that gives more importance to some well-chosen trajectories, typically those trajectories that manage to move earlier than others towards the critical region. Central limit theorem is established and numerical experiments are provided.