Training Neural Networks for Likelihood/Density Ratio Estimation

Understand

Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities.

• In classical approaches the two densities are assumed known or to belong to some known parametric family.
• In a data-driven version we replace this requirement with the availability of data sampled from the densities of interest.
• For most well known problems in Detection and Hypothesis testing we develop solutions by providing neural network based estimates of the likelihood ratio or its transformations.