5,809 research outputs found
The CONEstrip algorithm
Uncertainty models such as sets of desirable gambles and (conditional) lower previsions can be represented as convex cones. Checking the consistency of and drawing inferences from such models requires solving feasibility and optimization problems. We consider finitely generated such models. For closed cones, we can use linear programming; for conditional lower prevision-based cones, there is an efficient algorithm using an iteration of linear programs. We present an efficient algorithm for general cones that also uses an iteration of linear programs
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Sets of Priors Reflecting Prior-Data Conflict and Agreement
In Bayesian statistics, the choice of prior distribution is often debatable,
especially if prior knowledge is limited or data are scarce. In imprecise
probability, sets of priors are used to accurately model and reflect prior
knowledge. This has the advantage that prior-data conflict sensitivity can be
modelled: Ranges of posterior inferences should be larger when prior and data
are in conflict. We propose a new method for generating prior sets which, in
addition to prior-data conflict sensitivity, allows to reflect strong
prior-data agreement by decreased posterior imprecision.Comment: 12 pages, 6 figures, In: Paulo Joao Carvalho et al. (eds.), IPMU
2016: Proceedings of the 16th International Conference on Information
Processing and Management of Uncertainty in Knowledge-Based Systems,
Eindhoven, The Netherland
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