Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserve...
In this paper, air combat simulation data is reconstructed into a dynamic Bayesian network. It gives a compact probabilistic model that describes the progress of air combat and al...
This paper describes the use of dynamic Bayesian networks for the task of articulatory feature recognition. We show that by modeling the dependencies between a set of 6 multi-leve...
Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics...
This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactio...