This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from an...
In this paper we propose a credal representation of the interval probability associated with a belief function (b.f.), and show how it relates to several classical Bayesian transfo...
This paper presents a Bayesian framework for generating inverse-consistent inter-subject large deformation transformations between two multi-modal image sets of the brain. In this...
Although FPGA technology offers the potential of designing high performance systems at low cost, its programming model is prohibitively low level. To allow a novice signal/image pr...
Mokhtar Nibouche, Ahmed Bouridane, Fionn Murtagh, ...