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). p. 160605. 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 Cancer Center Program > Gene Regulation and Inheritance Program
CSHL labs > Kinney lab
Northwell Health
Depositing User: Matthew Dunn
Date: 19 October 2018
Date Deposited: 01 Nov 2018 19:54
Last Modified: 01 Dec 2020 15:08
PMCID: PMC6487661
Related URLs:
URI: https://repository.cshl.edu/id/eprint/37265

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