Density Estimation on Small Data Sets.

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.

SUBMITTER: Chen WC 

PROVIDER: S-EPMC6487661 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature