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  1. Proceedings of the 4th Workshop on Scala (SCALA '13)
  2. What are the Odds?: probabilistic programming in Scala
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An overview of the Leon verification system: verification by translation to recursive functions
CafeSat: a modern SAT solver for Scala
Scala macros: let our powers combine!: on how rich syntax and static types work with metaprogramming
A new concurrency model for Scala based on a declarative dataflow core
Open GADTs and declaration-site variance: a problem statement
Towards a tight integration of a functional web client language into Scala
Parsing graphs: applying parser combinators to graph traversals
Scalad: an interactive type-level debugger
An experimental study of the influence of dynamic compiler optimizations on Scala performance
Bridging islands of specialized code using macros and reified types
What are the Odds?: probabilistic programming in Scala
Dataflow constructs for a language extension based on the algebra of communicating processes

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What are the odds?

Newspaper Article

What are the Odds?: probabilistic programming in Scala

Content Provider ACM Digital Library
Author Rompf, Tiark Jonnalagedda, Manohar Amin, Nada Stucki, Sandro
Abstract Probabilistic programming is a powerful high-level paradigm for probabilistic modeling and inference. We present Odds, a small domain-specific language (DSL) for probabilistic programming, embedded in Scala. Odds provides first-class support for random variables and probabilistic choice, while reusing Scala's abstraction and modularity facilities for composing probabilistic computations and for executing deterministic program parts. Odds accurately represents possibly dependent random variables using a probability monad that models committed choice. This monadic representation of probabilistic models can be combined with a range of inference procedures. We present engines for exact inference, rejection sampling and importance sampling with look-ahead, but other types of solvers are conceivable as well. We evaluate Odds on several non-trivial probabilistic programs from the literature and we demonstrate how the basic probabilistic primitives can be used to build higher-level abstractions, such as rule-based logic programming facilities, using advanced Scala features.
Starting Page 1
Ending Page 9
Page Count 9
File Format PDF
ISBN 9781450320641
DOI 10.1145/2489837.2489848
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-07-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Probabilistic programming Scala Edsl Probability monad Probabilistic inference
Content Type Text
Resource Type Article
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