Logarithmic pooling of priors linked by a deterministic simulation model

We consider Bayesian inference when priors and likelihoods are both availab le for inputs and outputs of a deterministic simulation model. This problem is fundamentally related to the issue of aggregating (i.e., pooling) exper t opinion. We survey alternative strategies for aggregation, then describe computational approaches for implementing pooled inference for simulation m odels. Our approach (1) numerically transforms all priors to the same space ; (2) uses log pooling to combine priors; and (3) then draws standard Bayes ian inference. We use importance sampling methods, including an iterative, adaptive approach that is more flexible and has less bias in some instances than a simpler alternative. Our exploratory examples are the first steps t oward extension of the approach for highly complex and even noninvertible m odels.