  Probabilistic programming allows specification of probabilistic models in a
declarative manner. Recently, several new software systems and languages for
probabilistic programming have been developed on the basis of newly developed
and improved methods for approximate inference in probabilistic models. In this
contribution a probabilistic model for an idealized dark matter localization
problem is described. We first derive the probabilistic model for the inference
of dark matter locations and masses, and then show how this model can be
implemented using BUGS and Infer.NET, two software systems for probabilistic
programming. Finally, the different capabilities of both systems are discussed.
The presented dark matter model includes mainly non-conjugate factors, thus, it
is difficult to implement this model with Infer.NET.
