2004/01/08 by Alexander Gray, Alexander G. Gray, Gray, Alexander G. +12
Computer Science · Environmental Science · Physics and Astronomy · #Astrophysics (astro-ph) #Computational Physics and Python Applications #Data Analysis with R #FOS: Physical sciences #Soil Geostatistics and Mapping #astro-ph
paper · pdf · doi:10.48550/arxiv.astro-ph/0401121
4-page conference proceeding based on talk given at ADASS XIII, 13-15 October, 2003, Strasbourg
arxiv created 2004/01/08 · openalex publication_date 2004/01/08 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many fundamental statistical methods have become critical tools for scientific data analysis yet do not scale tractably to modern large datasets. This paper will describe very recent algorithms based on computational geometry which have dramatically reduced the computational complexity of 1) kernel density estimation (which also extends to nonparametric regression, classification, and clustering), and 2) the n-point correlation function for arbitrary n. These new multi-tree methods typically yield orders of magnitude in speedup over the previous state of the art for similar accuracy, making millions of data points tractable on desktop workstations for the first time.