Declarative modeling and bayesian inference of dark matter halos

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung


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.

TitelComputer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
Herausgeber (Verlag)Springer
AuflagePART 1
ISBN (Print)9783642538551
PublikationsstatusVeröffentlicht - 2013
Veranstaltung14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spanien
Dauer: 10 Feb. 201315 Feb. 2013


NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NummerPART 1
Band8111 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349


Konferenz14th International Conference on Computer Aided Systems Theory, Eurocast 2013
OrtLas Palmas de Gran Canaria


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