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.


Bayesian inference Strong prior-data agreement Prior-data conflict Imprecise probability Conjugate priors 



Gero Walter was supported by the Dinalog project “Coordinated Advanced Maintenance and Logistics Planning for the Process Industries” (CAMPI).


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Industrial EngineeringEindhoven University of TechnologyEindhovenNetherlands
  2. 2.Department of Mathematical SciencesDurham UniversityDurhamUK

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