Probabilistic programming

Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed automatically. Probabilistic programming attempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable. It can be used to create systems that help make decisions in the face of uncertainty. Programming languages following the probabilistic programming paradigm are referred to as "probabilistic programming languages" (PPLs).

Name	Extends from	Host language
Analytica		C++
bayesloop	Python	Python
Bean Machine	PyTorch	Python
Venture	Scheme	C++
BayesDB	SQLite, Python
PRISM	B-Prolog
Infer.NET	.NET Framework	.NET Framework
diff-SAT	Answer set programming, SAT (DIMACS CNF)
PSQL	SQL
BUGS		Component Pascal
Dyna	Prolog
Figaro	Scala	Scala
ProbLog	Prolog	Python
ProBT	C++, Python
Stan	BUGS	C++
Hakaru	Haskell	Haskell
BAli-Phy (software)	Haskell	C++
ProbCog		Java, Python
PyMC	Python	Python
Rainier	Scala	Scala
greta	TensorFlow	R
pomegranate	Python	Python
Lea	Python	Python
WebPPL	JavaScript	JavaScript
Picture	Julia	Julia
Turing.jl	Julia	Julia
Gen	Julia	Julia
Edward	TensorFlow	Python
TensorFlow Probability	TensorFlow	Python
Edward2	TensorFlow Probability	Python
Pyro	PyTorch	Python
NumPyro	JAX	Python
Birch		C++
PSI		D
Blang
MultiVerse	Python	Python
Anglican	Clojure	Clojure
Sandwood	Java	Java

• Reasoning about variables as probability distributions causes difficulties for novice programmers, but these difficulties can be addressed through use of Bayesian network visualizations and graphs of variable distributions embedded within the source code editor.
• As many PPLs rely on the specification of priors on the variables of interest, specifying informed priors is often difficult for novices. In some cases, libraries such as PyMC provide automated methods to find the parameterization of informed priors.

• Statistical relational learning
• Inductive programming
• Bayesian programming
• Plate notation

{{cite web|url=https://github.com/probcomp/bayeslite|title=BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself|work=GitHub|date=December 26, 2021}}</ref>\n<ref name=\"bayesloop\">{{Cite web|url=https://bayesloop.com/|title=bayesloop - Probabilistic programming framework|website=bayesloop.com}}</ref>\n<ref name=\"bayesloop2\">{{cite web|url=https://github.com/christophmark/bayesloop|title=GitHub -- bayesloop|website=[[GitHub]]|date=December 7, 2021}}</ref>\n<ref name=\"Analytica\">{{cite web|url=https://www.analytica.com|title=Analytica-- A Probabilistic Modeling Language|work=lumina.com}}</ref>\n<ref name=\"Venture\">{{cite web|url=http://probcomp.csail.mit.edu/venture/|title=Venture -- a general-purpose probabilistic programming platform|work=mit.edu|access-date=September 20, 2014|archive-url=https://web.archive.org/web/20160125130827/http://probcomp.csail.mit.edu/venture/|archive-date=January 25, 2016|url-status=dead}}</ref>\n<ref name=\"PRISM\">{{cite web|url=http://rjida.meijo-u.ac.jp/prism/|title=PRISM: PRogramming In Statistical Modeling|website=rjida.meijo-u.ac.jp|access-date=July 8, 2015|archive-url=https://web.archive.org/web/20150301155729/http://rjida.meijo-u.ac.jp/prism/|archive-date=March 1, 2015|url-status=dead}}</ref>\n<ref name=\"INFET\">{{cite web|url=http://research.microsoft.com/en-us/um/cambridge/projects/infernet/|title=Infer.NET|publisher=Microsoft|work=microsoft.com}}</ref>\n<ref name=\"diff-SAT\">{{cite web|url=https://github.com/MatthiasNickles/diff-SAT/|title=diff-SAT (probabilistic SAT/ASP)|website=[[GitHub]]|date=October 8, 2021}}</ref>\n<ref name=\"PSQL\">{{cite journal|title=PSQL: A query language for probabilistic relational data|doi=10.1016/S0169-023X(98)00015-9 | volume=28|journal=Data & Knowledge Engineering|pages=107–120|year = 1998|last1 = Dey|first1 = Debabrata|last2=Sarkar |first2=Sumit }}</ref>\n<ref name=\"BUGS\">{{cite web|url=http://www.mrc-bsu.cam.ac.uk/bugs/|title=The BUGS Project - MRC Biostatistics Unit|work=cam.ac.uk|access-date=January 12, 2011|archive-url=https://web.archive.org/web/20140314080841/http://www.mrc-bsu.cam.ac.uk/bugs/|archive-date=March 14, 2014|url-status=dead}}</ref>\n<ref name=\"Dyna\">{{cite web|url=https://www.dyna.org/|title=Dyna|website=www.dyna.org|access-date=January 12, 2011|archive-url=https://web.archive.org/web/20160117155947/http://dyna.org/|archive-date=January 17, 2016|url-status=dead}}</ref>\n<ref name=\"Figaro\">{{cite web|url=https://www.cra.com/figaro|title=Charles River Analytics - Probabilistic Modeling Services|work=cra.com|date=February 9, 2017}}</ref>\n<ref name=\"ProbLog\">{{cite web|url=http://dtai.cs.kuleuven.be/problog|title=ProbLog: Probabilistic Programming|website=dtai.cs.kuleuven.be}}</ref>\n<ref name=\"ProBT\">{{cite web|url=http://www.probayes.com/fr/Bayesian-Programming-Book/downloads/|title=ProbaYes - Ensemble, nous valorisations vos données|author=ProbaYes|work=probayes.com|access-date=November 26, 2013|archive-url=https://web.archive.org/web/20160305000751/http://www.probayes.com/fr/Bayesian-Programming-Book/downloads/|archive-date=March 5, 2016|url-status=dead}}</ref>\n<ref name=\"Stan\">{{cite web|url=http://mc-stan.org/|archive-url=https://web.archive.org/web/20120903133321/http://mc-stan.org/|url-status=dead|archive-date=2012-09-03|title=Stan|work=mc-stan.org}}</ref>\n<ref name=\"Hakaru\">{{cite web|url=https://hakaru-dev.github.io/|title=Hakaru Home Page|work=hakaru-dev.github.io/}}</ref>\n<ref name=\"BAli-Phy\">{{cite web|url=https://www.bali-phy.org/|title=BAli-Phy Home Page|work=bali-phy.org}}</ref>\n<ref name=\"ProbCog\">{{cite web|url=https://github.com/opcode81/ProbCog/wiki/Features|title=ProbCog|work=GitHub}}</ref>\n<ref name=\"PyMC\">{{cite web|url=https://docs.pymc.io/en/v3/|title=PyMC|author=PyMC devs|work=pymc-devs.github.io}}</ref>\n<ref name=\"Lea\">{{cite web|url=https://bitbucket.org/piedenis/lea|title=Lea Home Page|work=bitbucket.org}}</ref>\n<ref name=\"WebPPL\">{{cite web|url=https://dippl.org/|title=WebPPL Home Page|work=github.com/probmods/webppl}}</ref>\n<ref name=\"Turingjl\">{{cite web|url=https://github.com/yebai/Turing.jl|title=The Turing language for probabilistic programming|website=[[GitHub]]|date=December 28, 2021}}</ref>\n<ref name=\"beanmachine\">{{cite web|url=https://beanmachine.org|title=Bean Machine - A universal probabilistic programming language to enable fast and accurate Bayesian analysis|work=beanmachine.org}}</ref>"}},"i":0}}]}'>

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