Sequential Monte Carlo samplers for rare events

Abstract. We present novel sequential Monte Carlo (SMC) algorithms for the simulation of two broad classes of rare events which are suitable for the estimation of tail probabilities and probability density functions in the regions of rare events, as well as the simulation of rare system trajectories. These methods have some connection with previously proposed importance sampling (IS) and interacting particle system (IPS) methodologies, particularly those of [8, 4], but di.er significantly from previous approaches in a number of respects: especially in that they operate directly on the path space of the Markov process of interest