  This paper presents two case studies of data sets where the main inferential
goal is to characterize time-varying patterns in model structure. Both of these
examples are seen to be general cases of the so-called "partition problem,"
where auxiliary information (in this case, time) defines a partition over
sample space, and where different models hold for each element of the
partition. In the first case study, we identify time-varying graphical
structure in the covariance matrix of asset returns from major European equity
indices from 2006--2010. This structure has important implications for
quantifying the notion of financial contagion, a term often mentioned in the
context of the European sovereign debt crisis of this period. In the second
case study, we screen a large database of historical corporate performance in
order to identify specific firms with impressively good (or bad) streaks of
performance.
