TY - GEN
T1 - Declarative modeling and bayesian inference of dark matter halos
AU - Kronberger, Gabriel
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Bayesian Inference
KW - Dark Matter Localization
KW - Declarative Models
KW - Probabilistic Programming
UR - http://www.scopus.com/inward/record.url?scp=84892596731&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-53856-8_25
DO - 10.1007/978-3-642-53856-8_25
M3 - Conference contribution
SN - 9783642538551
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 195
EP - 202
BT - Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
PB - Springer
T2 - 14th International Conference on Computer Aided Systems Theory, Eurocast 2013
Y2 - 10 February 2013 through 15 February 2013
ER -