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...
We describe models and efficient algorithms for detecting groups (communities) functioning in communication networks which attempt to hide their functionality – hidden groups. O...
Jeffrey Baumes, Mark K. Goldberg, Malik Magdon-Ism...
—In this paper, we propose an analytical model to evaluate the hidden station effect on both non-saturation and saturation performance of the IEEE 802.11 Distributed Coordination...
In Proc. of IEEE Conf. on CVPR'03, Madison, Wisconsin, 2003 We propose a generative model approach to contour tracking against non-stationary clutter and to coping with occlu...
Motivated by the application of seismic inversion in the petroleum industry we consider a hidden Markov model with two hidden layers. The bottom layer is a Markov chain and given ...