Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches such as exponential ...
Shakir Mohamed, Katherine A. Heller, Zoubin Ghahra...
Bayesian networks (BN) are particularly well suited to capturing vague and uncertain knowledge. However, the capture of this knowledge and associated reasoning from human domain e...
Jonathan D. Pfautz, Zach Cox, Geoffrey Catto, Davi...
Background: Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variat...
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optim...