  Massive datasets in the gigabyte and terabyte range combined with the
availability of increasingly sophisticated statistical tools yield analyses at
the boundary of what is computationally feasible. Compromising in the face of
this computational burden by partitioning the dataset into more tractable sizes
results in stratified analyses, removed from the context that justified the
initial data collection. In a Bayesian framework, these stratified analyses
generate intermediate realizations, often compared using point estimates that
fail to account for the variability within and correlation between the
distributions these realizations approximate. However, although the initial
concession to stratify generally precludes the more sensible analysis using a
single joint hierarchical model, we can circumvent this outcome and capitalize
on the intermediate realizations by extending the dynamic iterative reweighting
MCMC algorithm. In doing so, we reuse the available realizations by reweighting
them with importance weights, recycling them into a now tractable joint
hierarchical model. We apply this technique to intermediate realizations
generated from stratified analyses of 687 influenza A genomes spanning 13 years
allowing us to revisit hypotheses regarding the evolutionary history of
influenza within a hierarchical statistical framework.
