Declarative modeling and bayesian inference of dark matter halos

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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.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
PublisherSpringer
Pages195-202
Number of pages8
EditionPART 1
ISBN (Print)9783642538551
DOIs
Publication statusPublished - 2013
Event14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spain
Duration: 10 Feb 201315 Feb 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Computer Aided Systems Theory, Eurocast 2013
CountrySpain
CityLas Palmas de Gran Canaria
Period10.02.201315.02.2013

Keywords

  • Bayesian Inference
  • Dark Matter Localization
  • Declarative Models
  • Probabilistic Programming

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