Probability boxes (pairs of cumulative distribution functions) are among the most popular models used in imprecise probability theory. In this paper, we provide new efficient tools to construct multivariate p-boxes and develop algorithms to draw inferences from them. For this purpose, we formalise and extend the theory of p-boxes using lower previsions. We allow p-boxes to be defined on arbitrary totally preordered spaces, hence thereby also admitting multivariate p-boxes. We discuss the construction of multivariate p-boxes under various independence assumptions. An example demonstrates the practical feasibility of our results.
The paper is available in the following formats:
Matthias Troffaes
Department of Mathematical Sciences
Durham University
Science Laboratories, South Road
Durham, DH1 3LE
Sebastien Destercke
UMR IATE, Campus INRA / Montpellier SupAgro 2, place Pierre Viala 34060 Montpellier Cedex 2
Matthias Troffaes | matthias.troffaes@gmail.com | |
Sebastien Destercke | sdestercke@gmail.com |
Send any remarks to isipta11@uibk.ac.at.