Diffuse Optical Tomography (DOT) poses a typical illposed inverse problem with limited number of measurements and inherently low spatial resolution. In this paper, we propose a hi...
Murat Guven, Birsen Yazici, Xavier Intes, Britton ...
This paper presents results from experiments, mathematical analysis, and simulations of a network of static and mobile sensors for detecting threats on city streets and in open ar...
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the E...
Modern Bayesian Network learning algorithms are timeefficient, scalable and produce high-quality models; these algorithms feature prominently in decision support model development...