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
In this paper we attempt to build upon past work on Interval Neural Networks, and provide a robust w...
In this paper we show how discrete and continuous variables can be combined using parametric conditi...
When we have only interval ranges [xi-,xi+] of sample values x1,...,xn, what is the interval [V-,V+]...
AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A...
AbstractWe present an extension of Bayesian networks to probability intervals, aiming at a more real...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
Traditionally, in science and engineering, measurement uncertainty is characterized by a probability...
DestD&al003International audienceProbability intervals are imprecise probability assignments over el...
Abstract. In many engineering situations, we need to make decisions under uncertainty. In some cases...
AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian...
AbstractThe use of interval probability theory (IPT) for uncertain inference is demonstrated. The ge...
This paper describes a robust and computationally feasible method to train and quantify the uncertai...
AbstractIn real-life decision analysis, the probabilities and utilities of consequences are in gener...
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence nod...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
In this paper we attempt to build upon past work on Interval Neural Networks, and provide a robust w...
In this paper we show how discrete and continuous variables can be combined using parametric conditi...
When we have only interval ranges [xi-,xi+] of sample values x1,...,xn, what is the interval [V-,V+]...
AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A...
AbstractWe present an extension of Bayesian networks to probability intervals, aiming at a more real...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
Traditionally, in science and engineering, measurement uncertainty is characterized by a probability...
DestD&al003International audienceProbability intervals are imprecise probability assignments over el...
Abstract. In many engineering situations, we need to make decisions under uncertainty. In some cases...
AbstractThis paper addresses the problem of computing posterior probabilities in a discrete Bayesian...
AbstractThe use of interval probability theory (IPT) for uncertain inference is demonstrated. The ge...
This paper describes a robust and computationally feasible method to train and quantify the uncertai...
AbstractIn real-life decision analysis, the probabilities and utilities of consequences are in gener...
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence nod...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
In this paper we attempt to build upon past work on Interval Neural Networks, and provide a robust w...
In this paper we show how discrete and continuous variables can be combined using parametric conditi...
When we have only interval ranges [xi-,xi+] of sample values x1,...,xn, what is the interval [V-,V+]...