  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.
