Generalized cross-entropy methods with applications to rare-event simulation and optimization
The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper, we investigate how these methods can be extended to provide a general non-parametric cross-entropy framework based on phi-divergence distance measures. We show how the chi(2) distance, in particular, yields a viable alternative to the Kullback-Leibler distance. The theory is illustrated with various examples from density estimation, rare-event simulation and continuous multi-extremal optimization.
generalized cross-entropy cross-entropy method rare-event simulation maximum entropy method stochastic optimization csisar's phi-divergence