Probabilistic programming language

From Wikipedia, the free encyclopedia
Jump to: navigation, search

A probabilistic programming language (PPL) is a programming language designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.[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 or 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 that in beta test.

Relational[edit]

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[edit]

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[edit]

In 2015 PPL a 50-line Picture 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.[11]

List of probabilistic programming languages[edit]

Name Extends from Host language
Venture[12] Scheme C++
Probabilistic-C[13] C C
Anglican[14] Scheme Clojure
IBAL[15] OCaml
PRISM[7] B-Prolog
Infer.NET[6] .NET Framework .NET Framework
dimple[4] MATLAB, Java
chimple[5] MATLAB, Java
BLOG[16] Java
PSQL[17] SQL
BUGS[8] n/a
FACTORIE[18] Scala
PMTK[19] MATLAB MATLAB
Alchemy[20] C++
Dyna[21] Prolog
Figaro[22] Scala
Church[23] Scheme Various: Javascript, Scheme
ProbLog[24] Prolog
ProBT[25] C++, Python
Stan (software)[9] n/a C++
Hakaru[26] Haskell Haskell
BAli-Phy (software) [27] Haskell C++
ProbCog[28] Java, Python
Tuffy[29] Java
PyMC[30] Python Python
Picture[11] Julia Julia

See also[edit]

Notes[edit]

  1. ^ "Probabilistic programming does in 50 lines of code what used to take thousands". phys.org. 2015-04-13. Retrieved 2015-04-13. 
  2. ^ "Probabilistic Programming". probabilistic-programming.org. 
  3. ^ "Short probabilistic programming machine-learning code replaces complex programs for computer-vision tasks". Kurzweil Accelerating Intelligence. 2015-04-13. Retrieved 2015-04-14. 
  4. ^ a b "Dimple Home Page". probprog.org. 
  5. ^ a b "Chimple Home Page". probprog.org. 
  6. ^ a b "Infer.NET". microsoft.com. Microsoft. 
  7. ^ a b PRISM Home Page
  8. ^ a b "The BUGS Project - MRC Biostatistics Unit". cam.ac.uk. 
  9. ^ a b "Stan". mc-stan.org. 
  10. ^ Pfeffer, Avrom (2014), Practical Probabilistic Programming, Manning Publications. p.28. ISBN 978-1 6172-9233-0
  11. ^ a b "Short probabilistic programming machine-learning code replaces complex programs for computer-vision tasks". KurzweilAI. April 13, 2015. Retrieved April 2015. 
  12. ^ "Venture -- a general-purpose probabilistic programming platform". mit.edu. 
  13. ^ "Probabilistic C". ox.ac.uk. 
  14. ^ "The Anglican Probabilistic Programming System". ox.ac.uk. 
  15. ^ IBAL Home Page
  16. ^ "Bayesian Logic (BLOG)". mit.edu. 
  17. ^ "PSQL: A query language for probabilistic relational data". doi:10.1016/S0169-023X(98)00015-9. 
  18. ^ "Factorie - Probabilistic programming with imperatively-defined factor graphs - Google Project Hosting". google.com. 
  19. ^ "PMTK3 - probabilistic modeling toolkit for Matlab/Octave, version 3 - Google Project Hosting". google.com. 
  20. ^ "Alchemy - Open Source AI". washington.edu. 
  21. ^ Dyna Home Page
  22. ^ "Charles River Analytics - Probabilistic Modeling Services". cra.com. 
  23. ^ "Church". mit.edu. 
  24. ^ ProbLog Home Page
  25. ^ ProbaYes. "ProbaYes - Ensemble, nous valorisations vos données". probayes.com. 
  26. ^ "Hakaru Home Page". indiana.edu. 
  27. ^ "BAli-Phy Home Page". bali-phy.org. 
  28. ^ "ProbCog". GitHub. 
  29. ^ "Tuffy: A Scalable Markov Logic Inference Engine". stanford.edu. 
  30. ^ PyMC devs. "PyMC3". pymc-devs.github.io. 

External links[edit]