Bayesian methods are now used in a variety of ways in discrete-event simulation. Applications include input modeling, response surface modeling, uncertainty analysis, and experime...
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...
Abstract: The problem of modeling and assessing an individual’s ability level is central to learning environments. Numerous approaches exists to this end. Computer Adaptive Testi...
Background: Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge. Resul...
Video segmentation requires the partitioning of a series of images into groups that are both spatially coherent and smooth along the time axis. We formulate segmentation as a Bayes...