Progressive Binary Partitioning for Performance Improvement in Multivariate Density Estimation | IEEE Conference Publication | IEEE Xplore

Progressive Binary Partitioning for Performance Improvement in Multivariate Density Estimation


Abstract:

This paper presents an algorithm for efficient multivariate density estimation, using a blockized implementation of the Bayesian sequential partitioning algorithm. We als...Show More

Abstract:

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.
Date of Conference: 26-29 May 2019
Date Added to IEEE Xplore: 01 May 2019
Print ISBN:978-1-7281-0397-6
Print ISSN: 2158-1525
Conference Location: Sapporo, Japan

References

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