We describe a radically new cache architecture and demonstrate that it offers a huge reduction in cache cost, size and power consumption whilst maintaining performance on a wide ra...
We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable dis...
As an extension of Bayesian network, module network is an appropriate model for inferring causal network of a mass of variables from insufficient evidences. However learning such ...
Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, bel...
With the continuing growth in the amount of genetic data, members of the bioinformatics community are developing a variety of data-mining applications to understand the data and d...