A Structural Learning Method for Graphical Models

This work is centred on investigating dependencies and representing learned structures as graphs. While there are a number of methods available for discrete and Gaussian random variables, there is no such method readily available for continuous variables that are non-Gaussian. For such methods to be reliable, it is necessary to have a way to measure pairwise and more importantly, conditional independence. In this work, an algorithm is created that uses both mutual information and a kernel method together to account for these dependencies and yield a graph that represents them. This method is then demonstrated through a simulation setting, comparing the results to an algorithm often used in Gaussian settings, additionally discussing future steps regarding this project