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.
Categories: physics.data-an cs.LG hep-ex