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Declarative Modeling and Bayesian Inference of Dark Matter Halos

  • Gabriel Kronberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)

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 in the 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.

Keywords

Declarative Models Probabilistic Programming Bayesian Inference Dark Matter Localization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gabriel Kronberger
    • 1
  1. 1.School of Informatics, Communications and MediaUniversity of Applied Sciences Upper AustriaHagenbergAustria

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