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We consider the problem of aggregating the judgements of a group of expertsto form a single prior distribution representing the judgements of the group.We develop a Bayesian hierarchical model to reconcile the judgements of thegroup of experts based on elicited quantiles for continuous quantities andprobabilities for one-off events. Previous Bayesian reconciliation methods havenot been used widely, if at all, in contrast to pooling methods andconsensus-based approaches. To address this we embed Bayesian reconciliationwithin the probabilistic Delphi method. The result is to furnish the outcome ofthe probabilistic Delphi method with a direct probabilistic interpretation,with the resulting prior representing the judgements of the decision maker. Wecan use the rationales from the Delphi process to group the experts for thehierarchical modelling. We illustrate the approach with applications to studiesevaluating erosion in embankment dams and pump failures in a water pumpingstation, and assess the properties of the approach using the TU Delft databaseof expert judgement studies. We see that, even using an off-the-shelfimplementation of the approach, it out-performs individual experts, equalweighting of experts and the classical method based on the log score.

Reconciliation of expert priors for quantities and events and application within the probabilistic Delphi method