Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph met...
Priya Mahadevan, Dmitri V. Krioukov, Kevin R. Fall...
Probability distributions are central tools for probabilistic modeling in data mining, and they lack in functional data analysis (FDA). In this paper we propose a probability dist...
The standard approach to information entropy applied to partitions of a universe is equivalently formulated as the entropy of the corresponding crisp identity resolutions, interpre...
This paper proposes a method of integrating two different concepts of belief in artificial intelligence: belief as a probability distribution and belief as a logical formula. The...
Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent...
— This paper presents an approach to approximate information content for active sensing tasks. The Unscented Transform is used to represent probability distributions by a set of ...
Model-based diagnosis is the field of research concerned with the problem of finding faults ms by reasoning with abstract models of the systems. Typically, such models offer a ...
We study the problem of finding an outlier-free subset of a set of points (or a probability distribution) in n-dimensional Euclidean space. As in [BFKV 99], a point x is defined t...
There is a range of potential applications of Machine Learning where it would be more useful to predict the probability distribution for a variable rather than simply the most lik...
Michael Carney, Padraig Cunningham, Jim Dowling, C...
This article proposes a new statistical model for fast 3D articulated body tracking, similar to the loose-limbed model, but where inter-frame coherence is taken into account by us...