Declarative Modeling and Bayesian Inference of Dark Matter Halos
This work addresses dark matter localization for astrophysics researchers, but it is incremental as it applies existing probabilistic programming tools to a specific domain problem.
The authors tackled the problem of modeling dark matter halos by deriving a probabilistic model for inferring their locations and masses, and demonstrated its implementation using BUGS and Infer.NET, noting challenges with non-conjugate factors in Infer.NET.
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.