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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== 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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== 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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