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Probabilistic programming language

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Title: Probabilistic programming language  
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Subject: Inductive programming, Stan (software), Functional logic programming, Non-structured programming, Structured programming
Collection: Probabilistic Models, Probabilistic Software
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Probabilistic programming language

A probabilistic programming language (PPL) is a [1] Probabilistic programming represents an attempt to "[unify] general purpose programming with probabilistic modeling."[2]

Probabilistic reasoning is a foundational technology of machine learning. It is used by companies such as Google, Amazon.com and Microsoft. Probabilistic reasoning has been used for predicting stock prices, recommending movies, diagnosing computers, detecting cyber intrusions and image detection.[3]

PPLs often extend from a basic language. The choice of underlying basic language depends on the similarity of the model to the basic language's ontology, as well as commercial considerations and personal preference. For instance, Dimple[4] and Chimple[5] are based on Java, Infer.NET is based on .NET framework,[6] while PRISM extends from Prolog.[7] However, some PPLs such as WinBUGS and Stan offer a self-contained language, with no obvious origin in another language.[8][9]

Several PPLs are in active development, including some in beta test.

Contents

  • Relational 1
  • Probabilistic programming 2
  • Applications 3
  • List of probabilistic programming languages 4
  • See also 5
  • Notes 6
  • External links 7

Relational

A probabilistic relational programming language (PRPL) is a PPL specially designed to describe and infer with probabilistic relational models (PRMs).

A PRM is usually developed with a set of algorithms for reducing, inference about and discovery of concerned distributions, which are embedded into the corresponding PRPL.

Probabilistic programming

Probabilistic programming creates systems that help make decisions in the face of uncertainty. Probabilistic reasoning combines knowledge of a situation with the laws of probability. Until recently, probabilistic reasoning systems have been limited in scope, and have not successfully addressed real world situations. Probabilistic programming is a new approach that makes probabilistic reasoning systems easier to build and more widely applicable.[10]

Applications

In 2015, a 50-line PPL computer vision program was used to generate 3D models of human faces based on 2D images of those faces. The approach used inverse graphics as the basis of its inferencing.[3] The implemention PPL language (and host language Julia language), made at MIT and made possible "in 50 lines of code what used to take thousands [whereas their experiments used their] probabilistic programming language they call Picture, which is an extension of Julia language, another language developed at MIT".[11][12] A paper on the Picture language, shown at the 2015 Computer Vision and Pattern Recognition conference, was awarded "Best Paper Honorable Mention".[13]

List of probabilistic programming languages

Name Extends from Host language
Venture[14] Scheme C++
Probabilistic-C[15] C C
Anglican[16] Scheme Clojure
IBAL[17] OCaml
PRISM[7] B-Prolog
Infer.NET[6] .NET Framework .NET Framework
dimple[4] MATLAB, Java
chimple[5] MATLAB, Java
BLOG[18] Java
PSQL[19] SQL
BUGS[8] R
FACTORIE[20] Scala
PMTK[21] MATLAB MATLAB
Alchemy[22] C++
Dyna[23] Prolog
Figaro[24] Scala
Church[25] Scheme Various: JavaScript, Scheme
ProbLog[26] Prolog
ProBT[27] C++, Python
Stan (software)[9] R C++
Hakaru[28] Haskell Haskell
BAli-Phy (software)[29] Haskell C++
ProbCog[30] Java, Python
Tuffy[31] Java
PyMC[32] Python Python
Lea[33] Python Python
Picture[3] Julia Julia

See also

Notes

  1. ^
  2. ^
  3. ^ a b c
  4. ^ a b
  5. ^ a b
  6. ^ a b
  7. ^ a b PRISM Home Page
  8. ^ a b
  9. ^ a b
  10. ^ Pfeffer, Avrom (2014), Practical Probabilistic Programming, Manning Publications. p.28. ISBN 978-1 6172-9233-0
  11. ^
  12. ^ http://www.theregister.co.uk/2015/04/14/mit_shows_off_machinelearning_script_to_make_creepy_heads/
  13. ^ http://www.pamitc.org/cvpr15/awards.php
  14. ^
  15. ^
  16. ^
  17. ^ IBAL Home Page
  18. ^
  19. ^
  20. ^
  21. ^
  22. ^
  23. ^ Dyna Home Page
  24. ^
  25. ^
  26. ^ ProbLog Home Page
  27. ^
  28. ^
  29. ^
  30. ^
  31. ^
  32. ^
  33. ^

External links

  • List of Probabilistic Model Mini Language Toolkits
  • Probabilistic programming wiki
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