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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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 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: | 07 Feb 2024 18:46 |
| PMCID: | PMC6487661 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/37265 |
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