A novel approach to computer vision is outlined, involving the use of imprecise probabilities to connect a deep learning based hierarchical vision system with both local feature de...
Although financial risk measurement is a largely investigated research area, its relationship with imprecise probabilities has been mostly overlooked. However, risk measures can b...
In real-life decision analysis, the probabilities and values of consequences are in general vague and imprecise. One way to model imprecise probabilities is to represent a probabi...
This paper discusses how numerically imprecise information can be modelled and how a risk evaluation process can be elaborated by integrating procedures for numerically imprecise ...
In this paper we try to clarify the notion of independence for imprecise probabilities. Our main point is that there are several possible definitions of independence which are app...
The paper summarizes the author's experience in dealing with the Dempster-Shafer theory relating to reliability assessments and demonstrates how to make component and system ...
This paper reviews algorithms for local computation with imprecise probabilities. These algorithms try to solve problems of inference calculation of conditional or unconditional p...
In this paper we consider decision making under hierarchical imprecise uncertainty models and derive general algorithms to determine optimal actions. Numerical examples illustrate...
A generalization of deFinetti’s Fundamental Theorem of Probability facilitates coherent assessment, by iterated natural extension, of imprecise probabilities or expectations, co...