  In this paper an original interacting particle system approach is developed
for studying Markov chains in rare event regimes. The proposed particle system
is theoretically studied through a genealogical tree interpretation of
Feynman--Kac path measures. The algorithmic implementation of the particle
system is presented. An estimator for the probability of occurrence of a rare
event is proposed and its variance is computed, which allows to compare and to
optimize different versions of the algorithm. Applications and numerical
implementations are discussed. First, we apply the particle system technique to
a toy model (a Gaussian random walk), which permits to illustrate the
theoretical predictions. Second, we address a physically relevant problem
consisting in the estimation of the outage probability due to polarization-mode
dispersion in optical fibers.
