This paper describes a deterministic approach to adaptive state and parameter estimation using a multiple model structure. In the set-up adopted, the plant of interest is described...
The neighborhood discovery and its maintenance are very important in wireless networks for any applications, especially for routing and every self-∗ algorithm. Neighbor nodes ar...
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...
The profile of a graph is an integer-valued parameter defined via vertex orderings; it is known that the profile of a graph equals the smallest number of edges of an interval supe...