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Practical Probabilistic Programming

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Inductive Logic Programming (ILP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6489))

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Abstract

Probabilistic programming promises to make probabilistic modeling easier by making it possible to create models using the power of programming languages, and by applying general-purpose algorithms to reason about models. We present a new probabilistic programming language named Figaro that was designed with practicality and usability in mind. Figaro can represent models naturally that have been difficult to represent in other languages, such as probabilistic relational models and models with undirected relationships with arbitrary constraints. An important feature is that the Figaro language and reasoning algorithms are embedded as a library in Scala. We illustrate the use of Figaro through a case study.

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References

  1. Pfeffer, A.: The Design and Implementation of IBAL: A General-Purpose Probabilistic Language. In: Getoor, L., Taskar, B. (eds.) Statistical Relational Learning. MIT Press, Cambridge (2007)

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  2. Goodman, N.D., Mansinghka, V.K., Roy, D., Bonawitz, K., Tenenbaum, J.B.: Church: A Language for Generative Models. In: Uncertainty in Artificial Intelligence (UAI) (2008)

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© 2011 Springer-Verlag Berlin Heidelberg

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Pfeffer, A. (2011). Practical Probabilistic Programming. In: Frasconi, P., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2010. Lecture Notes in Computer Science(), vol 6489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21295-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-21295-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21294-9

  • Online ISBN: 978-3-642-21295-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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