We have established a novel control system for combining the parallel execution of deterministic and non-deterministic medical imaging applications on a single platform, sharing t...
In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and co...
Bart Baesens, Michael Egmont-Petersen, Robert Cast...
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
?This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contribut...
— Recent progress in the algorithm as well as the processor power have made the dynamics simulation of complex kinematic chains more realistic in various fields such as human mo...