Generalized iLUCK-models are a way to define sets of priors based on conjugate priors, with the property that the set of posteriors, obtained by updating each prior in the set by Bayes' rule, is still easy to handle. Generalized iLUCK-models belong to the domain of imprecise probability (or interval probability models), allow to include non-stochastic uncertainty ('ambiguity') in Bayesian analysis, and lead to reasonable inferences in case of prior-data conflict.
|Author||Gero Walter [aut, cre], Norbert Krautenbacher [aut]|
|Date of publication||2013-08-27 15:53:15|
|Maintainer||Gero Walter <[email protected]>|
|Package repository||View on R-Forge|
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