Density Estimation on Small Data Sets

Chen, W. C., Tareen, A., Kinney, J. B. (October 2018) Density Estimation on Small Data Sets. Physical Review Letters, 121 (16). ISSN 00319007 (ISSN)

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URL: https://www.ncbi.nlm.nih.gov/pubmed/30387642
DOI: 10.1103/PhysRevLett.121.160605

Abstract

How might a smooth probability distribution be estimated with accurately quantified uncertainty from a limited amount of sampled data? Here we describe a field-theoretic approach that addresses this problem remarkably well in one dimension, providing an exact nonparametric Bayesian posterior without relying on tunable parameters or large-data approximations. Strong non-Gaussian constraints, which require a nonperturbative treatment, are found to play a major role in reducing distribution uncertainty. A software implementation of this method is provided. © 2018 authors. Published by the American Physical Society.

Item Type: Paper
Subjects: bioinformatics
bioinformatics > genomics and proteomics > computers > computer software
CSHL Authors:
Communities: CSHL labs > Kinney lab
Northwell Health
Depositing User: Matthew Dunn
Date: 19 October 2018
Date Deposited: 01 Nov 2018 19:54
Last Modified: 11 Jan 2019 20:01
Related URLs:
URI: http://repository.cshl.edu/id/eprint/37265

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