Probabilistic programming language
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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. Probabilistic programming represents an attempt to "[unify] general purpose programming with probabilistic modeling."
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.
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 and Chimple are based on Java, Infer.NET is based on .NET framework, while PRISM extends from Prolog. However, some PPLs such as WinBUGS and Stan offer a self-contained language, with no obvious origin in another language.
Several PPLs are in active development, including some in beta test.
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 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. Reasoning about variables as probability distributions causes difficulties for novice programmers, but these difficulties can be addressed through use of Bayesian network visualisations and graphs of variable distributions embedded within the source code editor.
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 program used inverse graphics as the basis of its inferencing, done by a PPL language they call Picture, (and the Julia host language) that 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". A paper on the Picture language, shown at the 2015 Computer Vision and Pattern Recognition conference, was awarded "Best Paper Honorable Mention".
List of probabilistic programming languages
|Name||Extends from||Host language|
|Infer.NET||.NET Framework||.NET Framework|
- "Probabilistic programming does in 50 lines of code what used to take thousands". phys.org. April 13, 2015. Retrieved 2015-04-13.
- "Probabilistic Programming". probabilistic-programming.org.
- "Short probabilistic programming machine-learning code replaces complex programs for computer-vision tasks". KurzweilAI. April 13, 2015. Retrieved April 2015. Check date values in:
- "Dimple Home Page". probprog.org.
- "Chimple Home Page". probprog.org.
- "Infer.NET". microsoft.com. Microsoft.
- PRISM Home Page
- "The BUGS Project - MRC Biostatistics Unit". cam.ac.uk.
- "Stan". mc-stan.org.
- Pfeffer, Avrom (2014), Practical Probabilistic Programming, Manning Publications. p.28. ISBN 978-1 6172-9233-0
- Gorinova, Maria I.; Sarkar, Advait; Blackwell, Alan F.; Syme, Don (2016-01-01). "A Live, Multiple-Representation Probabilistic Programming Environment for Novices". Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. CHI '16. New York, NY, USA: ACM: 2533–2537. doi:10.1145/2858036.2858221. ISBN 9781450333627.
- Hardesty, Larry (April 13, 2015). "Graphics in reverse".
- "Venture -- a general-purpose probabilistic programming platform". mit.edu.
- "Probabilistic C". ox.ac.uk.
- "The Anglican Probabilistic Programming System". ox.ac.uk.
- IBAL Home Page
- "Bayesian Logic (BLOG)". mit.edu.
- "PSQL: A query language for probabilistic relational data". doi:10.1016/S0169-023X(98)00015-9.
- "Factorie - Probabilistic programming with imperatively-defined factor graphs - Google Project Hosting". google.com.
- "PMTK3 - probabilistic modeling toolkit for Matlab/Octave, version 3 - Google Project Hosting". google.com.
- "Alchemy - Open Source AI". washington.edu.
- Dyna Home Page
- "Charles River Analytics - Probabilistic Modeling Services". cra.com.
- "Church". mit.edu.
- ProbLog Home Page
- ProbaYes. "ProbaYes - Ensemble, nous valorisations vos données". probayes.com.
- "Hakaru Home Page". indiana.edu.
- "BAli-Phy Home Page". bali-phy.org.
- "ProbCog". GitHub.
- Gamble Home Page|
- "Tuffy: A Scalable Markov Logic Inference Engine". stanford.edu.
- PyMC devs. "PyMC3". pymc-devs.github.io.
- "Lea Home Page". bitbucket.org.
- "WebPPL Home Page". github.com/probmods/webppl.
- "Turing: Rejuvenating probabilistic programming in Julia".