Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples

Charles Burton; Spencer Stubbs; Peter Onyisi

doi:10.1140/epjc/s10052-021-09469-y

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arXiv · 2103.13416

Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples

Charles Burton·Spencer Stubbs·Peter Onyisi

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Abstract

Mixture Density Networks (MDNs) can be used to generate probability density functions of model parameters $\boldsymbol{\theta}$ given a set of observables $\mathbf{x}$. In some applications, training data are available only for discrete values of a continuous parameter $\boldsymbol{\theta}$. 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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physics.data-an cs.LG hep-ex

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Charles Burton, Spencer Stubbs, Peter Onyisi. 2021-03-24. 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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