Abstract. Visualizing social networks presents challeges for both nodelink and adjacency matrix representations. Social networks are locally dense, which makes node-link displays u...
With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the ...
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Prev...
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Proof AnimationTM is a family of products for animating discrete event simulations. Proof is available in a variety of versions, including an inexpensive, student version, midsize...