New results in importance sampling

Importance sampling is a technique which can significantly reduce the numbe r of Monte Carlos necessary to accurately estimate the probability of low-p robability of occurrence events (e.g., the probability of false alarm P-F a ssociated with a given detection scheme). A new technique called the Cherno ff Importance Sampling Method is introduced. It is shown that the number of required Monte Carlos can be reduced by a factor of a Chernoff-like bound on P-F. In addition, techniques for choosing the multiplying factor of the distorted variance method (the most common method used in importance sampli ng) are presented.