The continuous improvement of fuel cycle simulators in conjunction with the increase computing capacities have led to a new scale scenario studies. Taking into consideration multiple variable parameters and observing their effect on evaluation criteria, these studies regroup several thousands trajectories paving different possible values for operational parameters. If global methods like sensit...

Autonomous fault management of network and distributed systems is a challenging research problem attracts many activities. Solving this heavily depends on expertise knowledge supporting tools for monitoring detecting defects automatically. Recent activities have focused machine learning techniques that scrutinize system output data mining abnormal events defects. This paper proposes two-phase d...

Bi-clustering is a useful approach in analyzing large biological data sets when the observations come from heterogeneous groups and have number of features. We outline general Bayesian tackling bi-clustering problems moderate to high dimensions propose three models on categorical which increase complexities their modeling distributions features across bi-clusters. Our proposed methods apply wid...