Special Article - Tools for Experiment and Theory
Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
Department of Physics, University of Texas, Austin, TX, USA
2 Physics Department, Rutgers University, New Brunswick, NJ, USA
Accepted: 20 July 2021
Published online: 28 July 2021
Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters given a set of observables . In some applications, training data are available only for discrete values of a continuous parameter . 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.
© The Author(s) 2021
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funded by SCOAP3