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

In this talk, I will present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for learning this structure are restricted to the discrete or Gaussian cases. Our new approach allows for more realistic and accurate descriptions of the distribution in question, and in turn better estimates of its sparse Markov structure. The algorithm relies on exploiting the connection between the sparsity of the graph and the sparsity of transport maps, which deterministically couple one probability measure to another. This is joint work with Ricardo Baptista and Youssef Marzouk.Non UBCUnreviewedAuthor affiliation: MITPostdoctora