Learning non-Gaussian graphical models via Hessian scores and triangular transport

no code implementations • 8 Jan 2021 • Ricardo Baptista, Youssef Marzouk, Rebecca E. Morrison, Olivier Zahm

Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables.

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting

no code implementations • NeurIPS 2017 • Rebecca E. Morrison, Ricardo Baptista, Youssef Marzouk

We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data.