Progressive Binary Partitioning for Performance Improvement in Multivariate Density Estimation

This paper presents an algorithm for efficient multivariate density estimation, using a blockized implementation of the Bayesian sequential partitioning algorithm. We also present a method for improving the performance of the blockized density estimation, by progressively updating the partitions. With progressive partitioning algorithm, each block uses the results from the previously processed blocks, and thus, as the simulation results show, it improves the performance of the blockized algorithm, both in terms of estimation accuracy and computation time.