Prior Specification

Specification of prior distributions for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts.

Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. In practice, however, we are still far from having usable prior elicitation tools that could significantly influence the way we build probabilistic models especially for high-dimensional problems.

We are approaching this challenge from two perspectives,

(a) by developing intuitive joint prior distributions that yield sensible prior predictions even in high-dimensional spaces and

(b) by building prior elicitation tools that transform expert knowledge in the data space into prior distributions on the model parameters that are consistent with that knowledge while satisfying additional probabilistic constraints.