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

Abstract

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

References in corpus (4)

• A Guide to Constraining Effective Field Theories with Machine Learning
• Interpolation between multi-dimensional histograms using a new non-linear moment morphing method
• Parameter Estimation using Neural Networks in the Presence of Detector Effects
• Parameter Inference from Event Ensembles and the Top-Quark Mass