  Sequential Monte Carlo methods which involve sequential importance sampling
and resampling are shown to provide a versatile approach to computing
probabilities of rare events. By making use of martingale representations of
the sequential Monte Carlo estimators, we show how resampling weights can be
chosen to yield logarithmically efficient Monte Carlo estimates of large
deviation probabilities for multidimensional Markov random walks.
