In this paper3 , we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show t...
X. Rosalind Wang, Joseph T. Lizier, Oliver Obst, M...
This work proposes the exploration of student’s information through the use of Bayesian Networks. By using thisapproach we aim to model the uncertainty inherent to the studentâ€...
The increasing role of communication networks in today’s society results in a demand for higher levels of network availability and reliability. At the same time, fault managemen...
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...
We describe the first tractable Gibbs sampling procedure for estimating phrase pair frequencies under a probabilistic model of phrase alignment. We propose and evaluate two nonpar...