  The Gaia mission will provide precise astrometry for an unprecedented number
of white dwarfs (WDs), encoding information on stellar evolution, Type Ia
supernovae progenitor scenarios, and the star formation and dynamical history
of the Milky Way. With such a large data set, it is possible to infer
properties of the WD population using only astrometric and photometric
information. We demonstrate a framework to accomplish this using a mock data
set with SDSS ugriz photometry and Gaia astrometric information. Our technique
utilises a Bayesian hierarchical model for inferring properties of a WD
population while also taking into account all observational errors of
individual objects, as well as selection and incompleteness effects. We
demonstrate that photometry alone can constrain the WD population's
distributions of temperature, surface gravity and phenomenological type, and
that astrometric information significantly improves determination of the WD
surface gravity distribution. We also discuss the possibility of identifying
unresolved binary WDs using only photometric and astrometric information.
