Declarative Modeling and Bayesian Inference of Dark Matter Halos

TL;DR

This paper presents a probabilistic model for localizing dark matter using Bayesian inference, implemented in BUGS and Infer.NET, highlighting the challenges with non-conjugate factors.

Contribution

It introduces a novel probabilistic model for dark matter localization and compares its implementation in two probabilistic programming systems, emphasizing the modeling challenges.

Findings

Model effectively captures dark matter localization

BUGS implementation is straightforward, Infer.NET faces difficulties with non-conjugate factors

Comparison highlights strengths and limitations of both systems

Abstract

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.