Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. It represents an attempt to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable.Pfeffer, Avrom (2014), ''Practical Probabilistic Programming'', Manning Publications. p.28. It can be used to create systems that help make decisions in the face of uncertainty. Programming languages used for probabilistic programming are referred to as "probabilistic programming languages" (PPLs).

Applications

Probabilistic reasoning has been used for a wide variety of tasks such as predicting stock prices, recommending movies, diagnosing computers, detecting cyber intrusions and image detection. However, until recently (partially due to limited computing power), probabilistic programming was limited in scope, and most inference algorithms had to be written manually for each task. Nevertheless, in 2015, a 50-line probabilistic

computer vision Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the hum ...

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 inference method, and was built using the Picture package in Julia. This made possible "in 50 lines of code what used to take thousands". The Gen probabilistic programming library (also written in Julia) has been applied to vision and robotics tasks. More recently, the probabilistic programming system Turing.jl has been applied in various pharmaceutical and economics applications. Probabilistic programming in Julia has also been combined with differentiable programming by combining the Julia package Zygote.jl with Turing.jl.

Probabilistic programming languages

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 In metaphysics, ontology is the philosophical study of being, as well as related concepts such as existence, becoming, and reality. Ontology addresses questions like how entities are grouped into categories and which of these entities exis ...

, as well as commercial considerations and personal preference. For instance, Dimple and Chimple are based on

Java Java (; id, Jawa, ; jv, ꦗꦮ; su, ) is one of the Greater Sunda Islands in Indonesia. It is bordered by the Indian Ocean to the south and the Java Sea to the north. With a population of 151.6 million people, Java is the world's List ...

Infer.NET Infer.NET is a free and open source .NET software library for machine learning. It supports running Bayesian inference in graphical models and can also be used for probabilistic programming. Overview Infer.NET follows a model-based approach and ...

is based on

.NET Framework The .NET Framework (pronounced as "''dot net"'') is a proprietary software framework developed by Microsoft that runs primarily on Microsoft Windows. It was the predominant implementation of the Common Language Infrastructure (CLI) until bein ...

, while PRISM extends from Prolog. However, some PPLs such as

WinBUGS WinBUGS is statistical software for Bayesian analysis using Markov chain Monte Carlo (MCMC) methods. It is based on the BUGS ( Bayesian inference Using Gibbs Sampling) project started in 1989. It runs under Microsoft Windows, though it can also ...

offer a self-contained language, that maps closely to the mathematical representation of the statistical models, with no obvious origin in another programming language. The language for winBUGS was implemented to perform Bayesian computation using Gibbs Sampling (and related algorithms). Although implemented in a relatively old programming language (Pascal), this language permits Bayesian inference for a wide variety of statistical models using a flexible computational approach. The same BUGS language may be used to specify Bayesian models for inference via different computational choices ("samplers") and conventions or defaults, using a standalone package winBUGS (or related R packages, rbugs and r2winbugs) and JAGS (Just Another Gibbs Sampler, another R package). More recently, other languages to support Bayesian model specification and inference allow different or more efficient choices for the underlying Bayesian computation, and are accessible from the R data analysis and programming environment, e.g.:

Stan Stan or STAN may refer to: People * Stan (given name), a list of people with the given name ** Stan Laurel (1890–1965), English comic actor, part of duo Laurel and Hardy * Stan (surname), a Romanian surname * Stan! (born 1964), American author ...

, NIMBLE and NUTS. The influence of the BUGS language is evident in these later languages, which even use the same syntax for some aspects of model specification. Several PPLs are in active development, including some in beta test. Two popular tools are Stan and

PyMC PyMC (formerly known as PyMC3) is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. It is a rewrite from scratch of the prev ...

Relational

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

probabilistic relational model Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational ...

s (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.

List of probabilistic programming languages

This list summarises the variety of PPLs that are currently available, and clarifies their origins.

Difficulty

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.

Notes

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GitHub GitHub, Inc. () is an Internet hosting service for software development and version control using Git. It provides the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous ...

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, date=December 28, 2021 {{cite web, url=http://topps.diku.dk/torbenm/troll.msp, title=Troll dice roller and probability calculator {{cite web, url=https://github.com/zz5013/pwCompiler, title=PWhile Compiler, work=GitHub, date=May 25, 2020 {{cite web, url=https://github.com/bradleygramhansen/PyLFPPL, title=LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models, work=ox.ac.uk, date=November 2, 2019 {{cite web, url=https://beanmachine.org, title=Bean Machine - A universal probabilistic programming language to enable fast and accurate Bayesian analysis, work=beanmachine.org

External links

List of Probabilistic Model Mini Language ToolkitsProbabilistic programming wiki
Probabilistic models Probabilistic software Programming paradigms