Density Estimation on Small Data Sets.
Phys Rev Lett
; 121(16): 160605, 2018 Oct 19.
Article
em En
| MEDLINE
| ID: mdl-30387642
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Phys Rev Lett
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Estados Unidos