Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image detection, the a priori dist...
Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this wo...
We consider a multi-agent system where each agent is equipped with a Bayesian network, and present an open framework for the agents to agree on a possible consensus network. The f...
Abstract— This paper deals with the use of Bayesian Networks to compute system reliability of complex systems under epistemic uncertainty. In the context of incompleteness of rel...
Inference of the network structure (e.g., routing topology) and dynamics (e.g., traffic matrices, link performance) is an important component in many network design and management ...