  We consider the task of forecasting an infinite sequence of future
observations based on some number of past observations, where the probability
measure generating the observations is "suspected" to satisfy one or more of a
set of incomplete models, i.e. convex sets in the space of probability
measures. This setting is in some sense intermediate between the realizable
setting where the probability measure comes from some known set of probability
measures (which can be addressed using e.g. Bayesian inference) and the
unrealizable setting where the probability measure is completely arbitrary. We
demonstrate a method of forecasting which guarantees that, whenever the true
probability measure satisfies an incomplete model in a given countable set, the
forecast converges to the same incomplete model in the (appropriately
normalized) Kantorovich-Rubinstein metric. This is analogous to merging of
opinions for Bayesian inference, except that convergence in the
Kantorovich-Rubinstein metric is weaker than convergence in total variation.
