  In astrophysics, we often aim to estimate one or more parameters for each
member object in a population and study the distribution of the fitted
parameters across the population. In this paper, we develop novel methods that
allow us to take advantage of existing software designed for such case-by-case
analyses to simultaneously fit parameters of both the individual objects and
the parameters that quantify their distribution across the population. Our
methods are based on Bayesian hierarchical modelling which is known to produce
parameter estimators for the individual objects that are on average closer to
their true values than estimators based on case-by-case analyses. We verify
this in the context of estimating ages of Galactic halo white dwarfs (WDs) via
a series of simulation studies. Finally, we deploy our new techniques on
optical and near-infrared photometry of ten candidate halo WDs to obtain
estimates of their ages along with an estimate of the mean age of Galactic halo
WDs of [11.25, 12.96] Gyr. Although this sample is small, our technique lays
the ground work for large-scale studies using data from the Gaia mission.
