Uncertainty processing methods are analysed from the viewpoint of their sensitivity to small variations of certainty factors. The analysis makes use of the algebraic theory which ...
In kernel density estimation methods, an approximation of the data probability density function is achieved by locating a kernel function at each data location. The smoothness of ...
Bayesian networks (BNs) have been widely used as a model for knowledge representation and probabilistic inferences. However, the single probability representation of conditional d...
A second-order hierarchical uncertainty model of a system of independent random variables is studied in the paper. It is shown that the complex nonlinear optimization problem for ...
—This paper addresses the independent component analysis (ICA) of quaternion random vectors. In particular, we focus on the Gaussian case and therefore only consider the quaterni...