AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A convex polytope representation of the interval probabilities is shown to make the problem intractable even for small parameters. A solution to this is to use the interval bounds directly in computations of the propagation algorithm. The algorithm presented leads to approximative results but has the advantage of being polynomial in time. It is shown that the method gives fairly good results
Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San ...
Abstract:- In this paper, we propose a probability-based approach to retrieve the most probable solu...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A...
AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian...
Traditionally, in science and engineering, measurement uncertainty is characterized by a probability...
Interval constraint propagation methods have been shown to be efficient and reliable to solve diffic...
AbstractWe present an extension of Bayesian networks to probability intervals, aiming at a more real...
When we have only interval ranges [xi-,xi+] of sample values x1,...,xn, what is the interval [V-,V+]...
Belief propagation on cyclic graphs is an efficient algorithm for computing approximate marginal pro...
Reasoning with a Bayesian network amounts to computing probability distri-butions for the network’s ...
One of the main problems of interval computations is to find an enclosure Y that contains the range ...
AbstractThe parameters of Markov chain models are often not known precisely. Instead of ignoring thi...
The computation of the inference corresponds to an NP-hard problem even for a single connected creda...
Abstract. In many engineering situations, we need to make decisions under uncertainty. In some cases...
Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San ...
Abstract:- In this paper, we propose a probability-based approach to retrieve the most probable solu...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A...
AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian...
Traditionally, in science and engineering, measurement uncertainty is characterized by a probability...
Interval constraint propagation methods have been shown to be efficient and reliable to solve diffic...
AbstractWe present an extension of Bayesian networks to probability intervals, aiming at a more real...
When we have only interval ranges [xi-,xi+] of sample values x1,...,xn, what is the interval [V-,V+]...
Belief propagation on cyclic graphs is an efficient algorithm for computing approximate marginal pro...
Reasoning with a Bayesian network amounts to computing probability distri-butions for the network’s ...
One of the main problems of interval computations is to find an enclosure Y that contains the range ...
AbstractThe parameters of Markov chain models are often not known precisely. Instead of ignoring thi...
The computation of the inference corresponds to an NP-hard problem even for a single connected creda...
Abstract. In many engineering situations, we need to make decisions under uncertainty. In some cases...
Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San ...
Abstract:- In this paper, we propose a probability-based approach to retrieve the most probable solu...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...