Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Abstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\the...

Full description

Bibliographic Details
Main Authors: Charles Burton, Spencer Stubbs, Peter Onyisi
Format: Article
Language:English
Published: SpringerOpen 2021-07-01
Series:European Physical Journal C: Particles and Fields
Online Access:https://doi.org/10.1140/epjc/s10052-021-09469-y
id doaj-42b642e65c024959bb56db60384144f4
record_format Article
spelling doaj-42b642e65c024959bb56db60384144f42021-08-01T11:12:41ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60441434-60522021-07-018171910.1140/epjc/s10052-021-09469-yMixture density network estimation of continuous variable maximum likelihood using discrete training samplesCharles Burton0Spencer Stubbs1Peter Onyisi2Department of Physics, University of TexasDepartment of Physics, University of TexasDepartment of Physics, University of TexasAbstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.https://doi.org/10.1140/epjc/s10052-021-09469-y
collection DOAJ
language English
format Article
sources DOAJ
author Charles Burton
Spencer Stubbs
Peter Onyisi
spellingShingle Charles Burton
Spencer Stubbs
Peter Onyisi
Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
European Physical Journal C: Particles and Fields
author_facet Charles Burton
Spencer Stubbs
Peter Onyisi
author_sort Charles Burton
title Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
title_short Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
title_full Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
title_fullStr Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
title_full_unstemmed Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
title_sort mixture density network estimation of continuous variable maximum likelihood using discrete training samples
publisher SpringerOpen
series European Physical Journal C: Particles and Fields
issn 1434-6044
1434-6052
publishDate 2021-07-01
description Abstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.
url https://doi.org/10.1140/epjc/s10052-021-09469-y
work_keys_str_mv AT charlesburton mixturedensitynetworkestimationofcontinuousvariablemaximumlikelihoodusingdiscretetrainingsamples
AT spencerstubbs mixturedensitynetworkestimationofcontinuousvariablemaximumlikelihoodusingdiscretetrainingsamples
AT peteronyisi mixturedensitynetworkestimationofcontinuousvariablemaximumlikelihoodusingdiscretetrainingsamples
_version_ 1721246187107909632

Similar Items