Probabilistic programming: Difference between revisions

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{{Short description|Software system for statistical models}}

{{Use mdy dates|date=September 2015}}

{{primary sources|date=December 2014}}

'''Probabilistic programming''' ('''PP''') is a [[programming paradigm]] inbased whichon the declarative specification of [[probabilistic model]]s are specified and inference, for thesewhich modelsinference is performed automatically.<ref name=physorg>{{cite news

| url=http://phys.org/news/2015-04-probabilistic-lines-code-thousands.html

| title=Probabilistic programming does in 50 lines of code what used to take thousands

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| date=April 13, 2015

| access-date=2015-04-13 }}

</ref> ItProbabilistic represents anprogramming attemptattempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable.<ref>{{cite web|url=http://probabilistic-programming.org/wiki/Home|title=Probabilistic Programming|work=probabilistic-programming.org|access-date=December 24, 2013|archive-url=https://web.archive.org/web/20160110035042/http://probabilistic-programming.org/wiki/Home|archive-date=January 10, 2016|url-status=dead}}</ref><ref name="Pfeffer2014">Pfeffer, Avrom (2014), ''Practical Probabilistic Programming'', Manning Publications. p.28. {{ISBN|978-1 6172-9233-0}}</ref> 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).

Programming languages used for probabilistic programming are referred to as "probabilistic programming languages" (PPLs).

== Applications ==

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The [[Gen (probabilistic programming)|Gen]] probabilistic programming library (also written in Julia) has been applied to vision and robotics tasks.<ref>{{Cite web|url=https://venturebeat.com/2019/06/27/mits-gen-programming-system-allows-users-to-easily-create-computer-vision-statistical-ai-and-robotics-programs/|title=MIT's Gen programming system flattens the learning curve for AI projects|date=2019-06-27|website=VentureBeat|language=en-US|access-date=2019-06-27}}</ref>

More recently, the probabilistic programming system [[Turing (probabilistic programming)|Turing.jl]] has been applied in various pharmaceutical<ref>{{Cite journal|last1=Semenova|first1=Elizaveta|last2=Williams|first2=Dominic P.|last3=Afzal|first3=Avid M.|last4=Lazic|first4=Stanley E.|date=2020-11-01|title=A Bayesian neural network for toxicity prediction|url=https://www.sciencedirect.com/science/article/pii/S2468111320300438|journal=Computational Toxicology|language=en|volume=16|pages=100133|doi=10.1016/j.comtox.2020.100133|s2cid=225362130|issn=2468-1113|url-access=subscription}}</ref> and economics applications.<ref name="pharma-turing">{{Citation|title= Predicting Drug-Induced Liver Injury with Bayesian Machine Learning |year= 2020 |doi= 10.1021/acs.chemrestox.9b00264 |url= https://pubs.acs.org/doi/10.1021/acs.chemrestox.9b00264|last1= Williams |first1= Dominic P. |last2= Lazic |first2= Stanley E. |last3= Foster |first3= Alison J. |last4= Semenova |first4= Elizaveta |last5= Morgan |first5= Paul |journal= Chemical Research in Toxicology |volume= 33 |issue= 1 |pages= 239–248 |pmid= 31535850 |s2cid= 202689667 |url-access= subscription }}</ref>

Probabilistic programming in Julia has also been combined with [[differentiable programming]] by combining the Julia package Zygote.jl with Turing.jl. <ref name="diffprog-zygote">{{cite arXiv|date=2019|title=∂P: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing|eprint=1907.07587|last1=Innes|first1=Mike|last2=Edelman|first2=Alan|last3=Fischer|first3=Keno|last4=Rackauckas|first4=Chris|last5=Saba|first5=Elliot|author6=Viral B Shah|last7=Tebbutt|first7=Will|class=cs.PL }}</ref>

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|-

|-Pomegranate || Python || Cython

|-

|-Infergo|| Go || Go

|-

| [[PyMC3|PyMC]]<ref name="PyMC"/> || Python|| Python

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|Python

|Python

|-

|Anglican<ref>{{Cite web |title=The Anglican Probabilistic Programming System |url=https://probprog.github.io/anglican/ |access-date=2024-10-07 |website=probprog.github.io}}</ref>

|[[Clojure]]

|[[Clojure]]

|}

== Difficulty ==

* Reasoning about variables as probability distributions causes difficulties for novice programmers, but these difficulties can be addressed through use of Bayesian network visualisationsvisualizations and graphs of variable distributions embedded within the source code editor.<ref>{{Cite book|last1=Gorinova|first1=Maria I.|last2=Sarkar|first2=Advait|last3=Blackwell|first3=Alan F.|last4=Syme|first4=Don|title=Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems |chapter=A Live, Multiple-Representation Probabilistic Programming Environment for Novices |date=2016-01-01|series=CHI '16|___location=New York, NY, USA|publisher=ACM|pages=2533–2537|doi=10.1145/2858036.2858221|isbn=9781450333627|s2cid=3201542}}</ref>

* 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.<ref>{{Cite web |title=pymc.find_constrained_prior — PyMC dev documentation |url=https://www.pymc.io/projects/docs/en/latest/api/generated/pymc.find_constrained_prior.html |access-date=2024-10-23 |website=www.pymc.io}}</ref>

==See also==

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==Notes==

{{Reflist|30em|refs=

<ref name="nova">{{cite book|url=https://dl.acm.org/citation.cfm?id=2627375|work=acm.org|series=Array'14|date=June 9, 2014|pages=8–13|doi=10.1145/2627373.2627375|isbn=9781450329378|s2cid=6748967 |chapter=NOVA: A Functional Language for Data Parallelism |title=Proceedings of ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming |last1=Collins |first1=Alexander |last2=Grewe |first2=Dominik |last3=Grover |first3=Vinod |last4=Lee |first4=Sean |last5=Susnea |first5=Adriana }}</ref>

<ref name="CuPPL">{{cite web|url=https://popl19.sigplan.org/event/lafi-2019-probabilistic-programming-with-cuppl|title=Probabilistic Programming with CuPPL|work=popl19.sigplan.org}}</ref>

<ref name="BAYESDB">{{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>

<ref name="bayesloop">{{Cite web|url=http://bayesloop.com/|title=bayesloop - Probabilistic programming framework|website=bayesloop.com}}</ref>

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<ref name="Analytica">{{cite web|url=http://www.analytica.com|title=Analytica-- A Probabilistic Modeling Language|work=lumina.com}}</ref>

<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>

<ref name="Anglican">{{cite web|url=https://github.com/probprog/anglican-infcomp|title=The Anglican Probabilistic Programming System|work=ox.ac.uk|date=January 6, 2021}}</ref>

<ref name="Probabilistic-C">{{cite web|url=http://www.robots.ox.ac.uk/~brooks/probabilistic-c/|title=Probabilistic C|work=ox.ac.uk|access-date=March 24, 2015|archive-url=https://web.archive.org/web/20160104201746/http://www.robots.ox.ac.uk/~brooks/probabilistic-c/|archive-date=January 4, 2016|url-status=dead}}</ref>

<ref name="IBAL">{{cite web|url=http://www.eecs.harvard.edu/~avi/IBAL/|title=IBAL Home Page|url-status=dead|archive-url=https://web.archive.org/web/20101226131239/http://www.eecs.harvard.edu/~avi/IBAL/|archive-date=December 26, 2010|df=mdy-all}}</ref>

<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>

<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>

<ref name="DMPL">{{cite web|url=https://github.com/analog-garage/dimple|title=Dimple Home Page|work=analog.com|date=July 2, 2021}}</ref>

<ref name="CHMPL">{{cite web|url=https://github.com/analog-garage/chimple|title=Chimple Home Page|work=analog.com|date=April 16, 2021}}</ref>

<ref name="BLOG">{{cite web|url=http://people.csail.mit.edu/milch/blog/|title=Bayesian Logic (BLOG)|work=mit.edu|url-status=dead|archive-url=https://web.archive.org/web/20110616214423/http://people.csail.mit.edu/milch/blog/|archive-date=June 16, 2011|df=mdy-all}}</ref>

<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>

<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>

<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>

<ref name="FACTORIE">{{cite web|url=http://code.google.com/p/factorie/|title=Factorie - Probabilistic programming with imperatively-defined factor graphs - Google Project Hosting|work=google.com}}</ref>

<ref name="PMTK">{{cite web|url=http://code.google.com/p/pmtk3/|title=PMTK3 - probabilistic modeling toolkit for Matlab/Octave, version 3 - Google Project Hosting|work=google.com}}</ref>

<ref name="Gamble">{{cite web|url=https://github.com/rmculpepper/gamble|title=gamble: Probabilistic Programming|first=Ryan|last=Culpepper|date=January 17, 2017|via=GitHub}}</ref>

<ref name="Alchemy">{{cite web|url=http://alchemy.cs.washington.edu/|title=Alchemy - Open Source AI|work=washington.edu}}</ref>

<ref name="Dyna">{{cite web|url=http://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>

<ref name="Figaro">{{cite web|url=http://www.cra.com/figaro|title=Charles River Analytics - Probabilistic Modeling Services|work=cra.com|date=February 9, 2017}}</ref>

<ref name="Church">{{cite web|url=http://projects.csail.mit.edu/church/wiki/Church|title=Church|work=mit.edu|access-date=April 8, 2013|archive-url=https://web.archive.org/web/20160114182510/http://projects.csail.mit.edu/church/wiki/Church|archive-date=January 14, 2016|url-status=dead}}</ref>

<ref name="ProbLog">{{cite web|url=http://dtai.cs.kuleuven.be/problog|title=ProbLog: Probabilistic Programming|website=dtai.cs.kuleuven.be}}</ref>

<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>

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<ref name="BAli-Phy">{{cite web|url=http://www.bali-phy.org/|title=BAli-Phy Home Page|work=bali-phy.org}}</ref>

<ref name="ProbCog">{{cite web|url=https://github.com/opcode81/ProbCog/wiki/Features|title=ProbCog|work=GitHub}}</ref>

<ref name="Tuffy">{{cite web|url=http://i.stanford.edu/hazy/tuffy/home|title=Tuffy: A Scalable Markov Logic Inference Engine|work=stanford.edu}}</ref>

<ref name="PyMC">{{cite web|url=https://docs.pymc.io/en/v3/|title=PyMC|author=PyMC devs|work=pymc-devs.github.io}}</ref>

<ref name="Lea">{{cite web|url=https://bitbucket.org/piedenis/lea|title=Lea Home Page|work=bitbucket.org}}</ref>

<ref name="WebPPL">{{cite web|url=http://dippl.org/|title=WebPPL Home Page|work=github.com/probmods/webppl}}</ref>

<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>

<ref name="Troll">{{Cite web|url=http://topps.diku.dk/torbenm/troll.msp|title=Troll dice roller and probability calculator|website=topps.diku.dk}}</ref>

<ref name="PWhile">{{cite web|url=https://github.com/zz5013/pwCompiler|title=PWhile Compiler|work=GitHub|date=May 25, 2020}}</ref>

<ref name="LFPPL">{{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}}</ref>

<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>

}}

== External links ==

*[https://www.cambridge.org/core/books/foundations-of-probabilistic-programming/819623B1B5B33836476618AC0621F0EE Foundations of Probabilistic Programming]

*[http://anyall.org/blog/2009/12/list-of-probabilistic-model-mini-language-toolkits/ List of Probabilistic Model Mini Language Toolkits]

*[https://web.archive.org/web/20160110035042/http://probabilistic-programming.org/wiki/Home Probabilistic programming wiki]

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