Importance sampling for stochastic simulations

Importance sampling is an effective technique for increasing the efficiency of Monte Carlo algorithms for the numerical analysis of integrals. The original random mechanism in the simulation can be replaced in order to solve problems arising from the simulation of stochastic systems for application to GI/G/1 queueing problems, including: discrete time Markov chains; continuous-time Markov chains; and generalized semi-Markov processes.