Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are used widely for uncertain reasoning in artiicial intelligence. In this paper, we investigate the problem of learning probabilistic networks with known structure and hidden variables. This is an important problem, because structu...

In the network design process, one goal is to select a network topology which is highly reliable. Although there is no universally accepted measure of reliability, the most widely used definition is a probabilistic one. The network is modelled as a probabilistic graph G = (V ,E) , in which V is a set of nodes representing sites in the network, and E is a collection of undirected edges represent...

Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artiicial intelligence. In this paper, we investigate the problem of learning probabilistic networks with known structure and hidden variables. This is an important ...

The basic frameworks and practical schemes of the Bayesian network and the belief propagation to the probabilistic image processing are reviewed. The probabilistic image processing is formulated by means of Bayesian statistics and Markov random fields. The system is regarded as one of Bayesian networks. In general, the Bayesian network has serious computational complexity because the probabilis...

A number of uncertain data models have been proposed, based on the notion of compact representations of probability distributions over possible worlds. In probabilistic relational models, tuples are annotated with probabilities or formulae over Boolean random variables. In probabilistic XML models, XML trees are augmented with nodes that specify probability distributions over their children. Bo...

The idea of probabilistic metric space was introduced by Menger and he showed that probabilistic metric spaces are generalizations of metric spaces. Thus, in this paper, we prove some of the important features and theorems and conclusions that are found in metric spaces. At the beginning of this paper, the distance distribution functions are proposed. These functions are essential in defining p...

We propose a probabilistic key predistribution scheme for wireless sensor networks, where keying materials are distributed to sensor nodes for secure communication. We use a two-tier approach in which there are two types of nodes: regular nodes and agent nodes. Agent nodes are more capable than regular nodes. Our node deployment model is zone-based such that the nodes that may end up with close...

the notion of a probabilistic metric  space  corresponds to thesituations when we do not know exactly the  distance.  probabilistic metric space was  introduced by karl menger. alsina, schweizer and sklar gave a general definition of  probabilistic normed space based on the definition of menger [1]. in this note we study the pn spaces which are  topological vector spaces and the open mapping an...