68 citations · 287 across the 26 of their papers we have counts for
4 papers · 1 filter
Smooth, identifiable supermodels of discrete DAG models with latent variables
Robin J. Evans, Thomas S. Richardson
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models a…
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans +1
We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG models that allow for certain…
One-Component Regular Variation and Graphical Modeling of Extremes
Adrien Hitz, Robin Evans
The problem of inferring the distribution of a random vector given that its norm is large requires modeling a homogeneous limiting density. We suggest an approach based on graphica…
Margins of discrete Bayesian networks
Robin J. Evans
Bayesian network models with latent variables are widely used in statistics and machine learning. In this paper we provide a complete algebraic characterization of Bayesian network…