In the preceding paper, Bayesian analysis was applied to the parameter estimation problem, given quadrature NMR data. Here Bayesian analysis is extended to the problem of selecting the model which is most probable in view of the data and all the prior information. In addition to the analytic calculation, two examples are given. The rst example demonstrates how to use Bayesian probability theory...

This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a ...

Recently, bayesian probability theory has been used at a number of experiments to fold uncertainties and interdependencies in the diagnostic data and forward models, together with prior knowledge of the state of the plasma, to increase accuracy of inferred physics variables. A new probabilistic framework, MINERVA, based on bayesian graphical models, has been used at JET and W7-AS to yield predi...

Causal relationships are present in many application domains. CP-logic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CP-theories from examples, and focusses on structure learning. The proposed approach is based on a transformation between CP-logic theories and Bayesian networks, that is, the method applies...