Mixture density network estimation of continuous variable maximum likelihood using discrete training samples
2021
Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$
given a set of observables $${\mathbf {x}}$$
. In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$
. 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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