An Adaptive Importance Sampling Procedure.
STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB
Pagination or Media Count:
Monte Carlo calculations often require generation of a random sample of n-dimensional points drawn from a specified multivariate probability distribution. We present an importance sampling technique that can often greatly improve the efficiency of an acceptancerejection generating method. The importance sampling function is defined as piecewise constant on a set of subregions, which are obtained by adaptively partitioning the sampling region so that the variation of density values within each subregion is relatively small. The partitioning strategy is based on multiparameter optimization to estimate the maximum and minimum of the original density function in each subregion. Author
- Statistics and Probability