Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the sensing mechanism simply selects sensing vectors independently at random from a ...
A random geometric graph Gn is constructed by taking vertices X1, . . . , Xn Rd at random (i.i.d. according to some probability distribution with a bounded density function) and...
The Kolmogorov–Smirnov test determines the consistency of empirical data with a particular probability distribution. Often, parameters in the distribution are unknown, and have ...
This paper introduces a general framework for defining the entropy of a graph. Our definition is based on a local information graph and on information functionals derived from the...
In order to structure a gene network, a score-based approach is often used. A score-based approach, however, is problematic because by assuming a probability distribution, one is ...
When merging belief sets from different agents, the result is normally a consistent belief set in which the inconsistency between the original sources is not represented. As proba...
Motivated by the need to reason about utilities, and inspired by the success of bayesian networks in representing and reasoning about probabilities, we introduce the notion of uti...
For many real-life Bayesian networks, common knowledge dictates that the output established for the main variable of interest increases with higher values for the observable varia...
Linda C. van der Gaag, Hans L. Bodlaender, A. J. F...