  Walley's Imprecise Dirichlet Model (IDM) for categorical i.i.d. data extends
the classical Dirichlet model to a set of priors. It overcomes several
fundamental problems which other approaches to uncertainty suffer from. Yet, to
be useful in practice, one needs efficient ways for computing the
imprecise=robust sets or intervals. The main objective of this work is to
derive exact, conservative, and approximate, robust and credible interval
estimates under the IDM for a large class of statistical estimators, including
the entropy and mutual information.
