Sets of Priors Reflecting Prior-Data Conflict and Agreement
Abstract
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.
Keywords
Cite
@article{arxiv.1610.07217,
title = {Sets of Priors Reflecting Prior-Data Conflict and Agreement},
author = {Gero Walter and Frank P. A. Coolen},
journal= {arXiv preprint arXiv:1610.07217},
year = {2016}
}
Comments
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 Netherlands