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

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

Automated summary

In this study, the use of Mixture Density Networks (MDNs) for parameter estimation in situations where training data is only available for discrete values of a continuous parameter is demonstrated. The origins of biases are discussed, and corrective methods for each issue are proposed to address performance-limiting problems that may arise.