75,999 research outputs found
The Libra Toolkit for Probabilistic Models
The Libra Toolkit is a collection of algorithms for learning and inference
with discrete probabilistic models, including Bayesian networks, Markov
networks, dependency networks, and sum-product networks. Compared to other
toolkits, Libra places a greater emphasis on learning the structure of
tractable models in which exact inference is efficient. It also includes a
variety of algorithms for learning graphical models in which inference is
potentially intractable, and for performing exact and approximate inference.
Libra is released under a 2-clause BSD license to encourage broad use in
academia and industry
Declarative Modeling and Bayesian Inference of Dark Matter Halos
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.Comment: Presented at the Workshop "Intelligent Information Processing",
EUROCAST2013. To appear in selected papers of Computer Aided Systems Theory -
EUROCAST 2013; Volumes Editors: Roberto Moreno-D\'iaz, Franz R. Pichler,
Alexis Quesada-Arencibia; LNCS Springe
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