@inproceedings{12f9593105d941cf94c9a5f3ee6c9113,

title = "Completely monotone outer approximations of lower probabilities on finite possibility spaces",

abstract = "Drawing inferences from general lower probabilities on finite possibility spaces usually involves solving linear programming problems. For some applications this may be too computationally demanding. Some special classes of lower probabilities allow for using computationally less demanding techniques. One such class is formed by the completely monotone lower probabilities, for which inferences can be drawn efficiently once their M{\"o}bius transform has been calculated. One option is therefore to draw approximate inferences by using a completely monotone approximation to a general lower probability; this must be an outer approximation to avoid drawing inferences that are not implied by the approximated lower probability. In this paper, we discuss existing and new algorithms for performing this approximation, discuss their relative strengths and weaknesses, and illustrate how each one works and performs.",

keywords = "Belief functions, Complete monotonicity, lower probabilities, M{\"o}bius transform, Outer approximation",

author = "Erik Quaeghebeur",

year = "2011",

doi = "10.1007/978-3-642-22833-9_20",

language = "English",

isbn = "9783642228322",

series = "Advances in Intelligent and Soft Computing",

pages = "169--178",

editor = "Shoumei Li and Li Guan and Xia Wang and Yoshiaki Okazaki and Jun Kawabe and Toshiaki Murofushi",

booktitle = "Nonlinear Mathematics for Uncertainty and its Applications",

}