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DOE OSTI · 1810593

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

Burton, Charles (ORCID:0000000234276537)·Stubbs, Spencer·Onyisi, Peter
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Abstract

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.

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Burton, Charles (ORCID:0000000234276537), Stubbs, Spencer, Onyisi, Peter. 2021-07-28. Mixture density network estimation of continuous variable maximum likelihood using discrete training samples. https://doi.org/10.1140/epjc%2Fs10052-021-09469-y

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