Title: Score matching for high-dimensional graphical models
Authors: Mathias Drton - Technical University of Munich (Germany) [presenting]
Abstract: A common challenge in the estimation of parameters of multivariate probability density functions is the intractability of the normalizing constant. For continuous data, the score matching method provides a way to circumvent this issue and is particularly convenient for graphical modeling. We will present regularized score matching methods for high-dimensional and possibly non-Gaussian graphical models. In particular, we will discuss generalizations of score matching for observations that are non-negative or otherwise constrained in their support.