Reproducible Parameter Inference Using Bagged Posteriors
Jonathan H. Huggins Jeffrey W. Miller
Under model misspecification, it is known that Bayesian posteriors often do
not properly quantify uncertainty about true or pseudo-true parameters. Even
more fundamentally, misspecification leads to a lack of reproducibility in the
sense that the same model will yield contradictory posteriors on independent
data sets from the true distribution. To define a criterion for reproducible
uncertainty quantification under misspecification, we consider the probability
that two confidence sets constructed from independent data sets have nonempty
overlap, and we establish a lower bound on this overlap probability that holds
for any valid confidence sets. We prove that credible sets from the standard
posterior can strongly violate this bound, particularly in high-dimensional
settings (i.e., with dimension increasing with sample size), indicating that it
is not internally coherent under misspecification. To improve reproducibility
in an easy-to-use and widely applicable way, we propose to apply bagging to the
Bayesian posterior ("BayesBag"'); that is, to use the average of posterior
distributions conditioned on bootstrapped datasets. We motivate BayesBag from
first principles based on Jeffrey conditionalization and show that the bagged
posterior typically satisfies the overlap lower bound. Further, we prove a
Bernstein--Von Mises theorem for the bagged posterior, establishing its
asymptotic normal distribution. We demonstrate the benefits of BayesBag via
simulation experiments and an application to crime rate prediction.