An important decision problem in many software projects is when to stop testing and release software for use. For many software products, time to market is critical and therefore ...
Norman E. Fenton, Martin Neil, William Marsh, Pete...
Abstract. We describe a probabilistic model, implemented as a dynamic Bayesian network, that can be used to predict nucleosome positioning along a chromosome based on one or more g...
Sheila M. Reynolds, Zhiping Weng, Jeff A. Bilmes, ...
The evolution of dependencies in information hierarchies can be modeled by sequences of compound digraphs with edge weights. In this paper we present a novel approach to visualize...
Background: This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data se...
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...