We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is des...
Given a random set coming from the imprecise observation of a random variable, we study how to model the information about the distribution of this random variable. Specifically,...
We present the application of a recently introduced nonparametric predictive inferential method to compare two groups of data, consisting of observed event times and right-censori...
This paper studies the possibility of representing lower previsions by continuous linear functionals. We prove the existence of a linear isomorphism between the linear space spann...
For many problems there is only suf£cient prior information for a Bayesian decision maker to identify a class of possible prior distributions. In such cases it is of interest to ...
Dempster’s rule for combining two belief functions assumes the independence of the sources of information. If this assumption is questionable, I suggest to use the least speci...
We apply random set theory to an analysis of future climate change. Bounds on cumulative probability are used to quantify uncertainties in natural and socio-economic factors that ...
Let be a preference relation on a convex set F. Necessary and sufficient conditions are given that guarantee the existence of a set {ul} of affine utility functions on F such th...
In this paper the judgement consisting in choosing a function that is believed to dominate the true probability distribution of a continuous random variable is explored. This kind...
This paper reviews recent results obtained in the medical diagnosis field by adding to a coherent inference process qualitative constraints. Such further considerations turn out ...