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 1-divergence distance measures. We show how the 2 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.