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词条 Probabilistic programming language
释义

  1. Relational

  2. Probabilistic programming

  3. Applications

  4. List of probabilistic programming languages

  5. See also

  6. Notes

  7. External links

{{Use mdy dates|date=September 2015}}{{primary sources|date=December 2014}}

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.[1] Probabilistic programming represents an attempt to "[unify] general purpose programming with probabilistic modeling."[2]

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.[3]

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[4] and Chimple[5] are based on Java, Infer.NET is based on .NET framework,[6] while PRISM extends from Prolog.[7] However, some PPLs such as WinBUGS and Stan offer a self-contained language, with no obvious origin in another language.[8][9]

Several PPLs are in active development, including some in beta test.

Relational

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

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.[4] 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.[5]

Applications

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.[3] This 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".[6][7] A paper on the Picture language, shown at the 2015 Computer Vision and Pattern Recognition conference, was awarded "Best Paper Honorable Mention".[8]

List of probabilistic programming languages

{{expand list|date=February 2011}}
Name Extends from Host language
Analytica[16] C++
bayesloop[17][18] Python Python
CuPPL[19] NOVA
Venture[21] Scheme C++
Probabilistic-C[22] C C
Anglican[23] Clojure Clojure
IBAL[24] OCaml
BayesDB[25] SQLite, Python
PRISM[7] B-Prolog
Infer.NET[6] .NET Framework .NET Framework
dimple[4] MATLAB, Java
chimple[5] MATLAB, Java
BLOG[30] Java
PSQL[31] SQL
BUGS[8]
FACTORIE[33] Scala
PMTK[34] MATLAB MATLAB
Alchemy[35] C++
Dyna[36] Prolog
Figaro[37] Scala
Church[38] Scheme Various: JavaScript, Scheme
ProbLog[39] Prolog Python, Jython
ProBT[40] C++, Python
Stan[9] C++
Hakaru[42] Haskell Haskell
BAli-Phy (software)[43] Haskell C++
ProbCog[44] Java, Python
Gamble[45] Racket
PWhile[46] While Python
Tuffy[47] Java
PyMC3[48] Python, Theano Python
PyMC4[9] Python, TensorFlow Probability Python
greta[10]TensorFlowR
pomegranate[11]PythonPython
Lea[52] Python Python
WebPPL[53]JavaScriptJavaScript
Picture[3] Julia Julia
Turing.jl[55] Julia Julia
Troll[56]Moscow ML
Edward[12]TensorFlowPython
TensorFlow Probability[13]TensorFlowPython
Edward2[14]TensorFlow ProbabilityPython
Pyro[15]PyTorchPython
Saul[16]ScalaScala
RankPL[17]Java
Birch[18]C++

See also

  • Statistical relational learning
  • Inductive programming
  • Bayesian programming

Notes

1. ^{{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 | work=phys.org | date=April 13, 2015 | accessdate=2015-04-13 }}
2. ^{{cite web|url=http://probabilistic-programming.org/wiki/Home|title=Probabilistic Programming|work=probabilistic-programming.org}}
3. ^1 2 {{cite news|url = http://www.kurzweilai.net/short-probabilistic-programming-machine-learning-code-replaces-complex-programs-for-computer-vision-tasks|title = Short probabilistic programming machine-learning code replaces complex programs for computer-vision tasks|last = |first = |date = April 13, 2015|work = KurzweilAI|access-date = 27 Nov 2017|via = }}
4. ^Pfeffer, Avrom (2014), Practical Probabilistic Programming, Manning Publications. p.28. {{ISBN|978-1 6172-9233-0}}
5. ^{{Cite book|last=Gorinova|first=Maria I.|last2=Sarkar|first2=Advait|last3=Blackwell|first3=Alan F.|last4=Syme|first4=Don|date=2016-01-01|title=A Live, Multiple-Representation Probabilistic Programming Environment for Novices|journal=Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems|series=CHI '16|location=New York, NY, USA|publisher=ACM|pages=2533–2537|doi=10.1145/2858036.2858221|isbn=9781450333627}}
6. ^{{cite web |url=http://news.mit.edu/2015/better-probabilistic-programming-0413 |title=Graphics in reverse |first=Larry |last=Hardesty |date=April 13, 2015}}
7. ^{{cite web|url=https://www.theregister.co.uk/2015/04/14/mit_shows_off_machinelearning_script_to_make_creepy_heads/|title=MIT shows off machine-learning script to make CREEPY HEADS|publisher=}}
8. ^{{cite web|url=http://www.pamitc.org/cvpr15/awards.php|title=CVPR 2015 Webpage - Awards|website=www.pamitc.org}}
9. ^{{Cite web|url=https://medium.com/@pymc_devs/theano-tensorflow-and-the-future-of-pymc-6c9987bb19d5|title=Theano, TensorFlow and the Future of PyMC|last=Developers|first=PyMC|date=2018-05-17|website=PyMC Developers|access-date=2019-01-25}}
10. ^{{Cite web|url=https://greta-dev.github.io/greta/|title=greta: simple and scalable statistical modelling in R|last=|first=|date=|website=GitHub|archive-url=|archive-date=|dead-url=|access-date=2018-10-02}}
11. ^{{Cite web|url=https://pomegranate.readthedocs.io/en/latest/index.html|title=Home — pomegranate 0.10.0 documentation|website=pomegranate.readthedocs.io|language=en|access-date=2018-10-02}}
12. ^{{Cite web|url=http://edwardlib.org/|title=Edward – Home|website=edwardlib.org|access-date=2017-01-17}}
13. ^{{Cite web|url=https://medium.com/tensorflow/introducing-tensorflow-probability-dca4c304e245|title=Introducing TensorFlow Probability|last=TensorFlow|date=2018-04-11|website=TensorFlow|access-date=2018-10-02}}
14. ^{{Cite web|url=https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/edward2|title='Edward2' TensorFlow Probability module|last=|first=|date=|website=GitHub|language=en|archive-url=|archive-date=|dead-url=|access-date=2018-10-02}}
15. ^{{Cite web|url=http://pyro.ai|title=Pyro|website=pyro.ai|language=en|access-date=2018-02-09}}
16. ^{{Cite web | url= https://cogcomp.org/page/software_view/Saul|title=CogComp - Home}}
17. ^{{Citation|last=Rienstra|first=Tjitze|title=RankPL: A qualitative probabilistic programming language based on ranking theory|date=2018-01-18|url=https://github.com/tjitze/RankPL|accessdate=2018-01-18}}
18. ^{{Cite web|url=http://birch-lang.org/|title=Probabilistic Programming in Birch|website=birch-lang.org|access-date=2018-04-20}}
19. ^1 {{cite web|url=https://popl19.sigplan.org/event/lafi-2019-probabilistic-programming-with-cuppl|title=Probabilistic Programming with CuPPL|work=popl19.sigplan.org}}
20. ^1 {{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}}
21. ^1 {{cite web|url=http://bayesloop.com/|title=bayesloop: Probabilistic programming framework that facilitates objective model selection for time-varying parameter models}}
22. ^1 {{cite web|url=https://github.com/christophmark/bayesloop|title=GitHub -- bayesloop}}
23. ^1 {{cite web|url=http://www.analytica.com|title=Analytica-- A Probabilistic Modeling Language|work=lumina.com}}
24. ^1 {{cite web|url=http://probcomp.csail.mit.edu/venture/|title=Venture -- a general-purpose probabilistic programming platform|work=mit.edu}}
25. ^1 {{cite web|url=https://github.com/probprog/anglican-infcomp|title=The Anglican Probabilistic Programming System|work=ox.ac.uk}}
26. ^1 {{cite web|url=http://www.robots.ox.ac.uk/~brooks/probabilistic-c/|title=Probabilistic C|work=ox.ac.uk}}
27. ^1 {{cite web|url=http://www.eecs.harvard.edu/~avi/IBAL/|title=IBAL Home Page|publisher=|deadurl=yes|archiveurl=https://web.archive.org/web/20101226131239/http://www.eecs.harvard.edu/~avi/IBAL/|archivedate=December 26, 2010|df=mdy-all}}
28. ^1 2 {{cite web|url=http://rjida.meijo-u.ac.jp/prism/|title=PRISM: PRogramming In Statistical Modeling|website=rjida.meijo-u.ac.jp}}
29. ^1 2 {{cite web|url=http://research.microsoft.com/en-us/um/cambridge/projects/infernet/|title=Infer.NET|publisher=Microsoft|work=microsoft.com}}
30. ^1 2 {{cite web|url=https://github.com/analog-garage/dimple|title=Dimple Home Page|work=analog.com}}
31. ^1 2 {{cite web|url=https://github.com/analog-garage/chimple|title=Chimple Home Page|work=analog.com}}
32. ^1 {{cite web|url=http://people.csail.mit.edu/milch/blog/|title=Bayesian Logic (BLOG)|work=mit.edu|deadurl=yes|archiveurl=https://web.archive.org/web/20110616214423/http://people.csail.mit.edu/milch/blog/|archivedate=June 16, 2011|df=mdy-all}}
33. ^1 {{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 }}
34. ^1 2 {{cite web|url=http://www.mrc-bsu.cam.ac.uk/bugs/|title=The BUGS Project - MRC Biostatistics Unit|work=cam.ac.uk}}
35. ^1 {{cite web|url=http://code.google.com/p/factorie/|title=Factorie - Probabilistic programming with imperatively-defined factor graphs - Google Project Hosting|work=google.com}}
36. ^1 {{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}}
37. ^1 {{cite web|url=https://github.com/rmculpepper/gamble|title=gamble: Probabilistic Programming|first=Ryan|last=Culpepper|date=January 17, 2017|publisher=|via=GitHub}}
38. ^1 {{cite web|url=http://alchemy.cs.washington.edu/|title=Alchemy - Open Source AI|work=washington.edu}}
39. ^1 {{cite web|url=http://www.dyna.org/|title=Dyna|website=www.dyna.org}}
40. ^1 {{cite web|url=http://www.cra.com/figaro|title=Charles River Analytics - Probabilistic Modeling Services|work=cra.com}}
41. ^1 {{cite web|url=http://projects.csail.mit.edu/church/wiki/Church|title=Church|work=mit.edu}}
42. ^1 {{cite web|url=http://dtai.cs.kuleuven.be/problog|title=ProbLog: Probabilistic Programming|website=dtai.cs.kuleuven.be}}
43. ^1 {{cite web|url=http://www.probayes.com/fr/Bayesian-Programming-Book/downloads/|title=ProbaYes - Ensemble, nous valorisations vos données|author=ProbaYes|work=probayes.com}}
44. ^1 2 {{cite web|url=http://mc-stan.org/|title=Stan|work=mc-stan.org}}
45. ^1 {{cite web|url=https://hakaru-dev.github.io/|title=Hakaru Home Page|work=hakaru-dev.github.io/}}
46. ^1 {{cite web|url=http://www.bali-phy.org/|title=BAli-Phy Home Page|work=bali-phy.org}}
47. ^1 {{cite web|url=https://github.com/opcode81/ProbCog/wiki/Features|title=ProbCog|work=GitHub}}
48. ^1 {{cite web|url=http://i.stanford.edu/hazy/tuffy/home|title=Tuffy: A Scalable Markov Logic Inference Engine|work=stanford.edu}}
49. ^1 {{cite web|url=https://pymc-devs.github.io/pymc3/|title=PyMC3|author=PyMC devs|work=pymc-devs.github.io}}
50. ^1 {{cite web|url=https://bitbucket.org/piedenis/lea|title=Lea Home Page|work=bitbucket.org}}
51. ^1 {{cite web|url=http://dippl.org/|title=WebPPL Home Page|work=github.com/probmods/webppl}}
52. ^1 {{cite web|url=https://github.com/yebai/Turing.jl|title=The Turing language for probabilistic programming}}
53. ^1 {{cite web|url=http://topps.diku.dk/torbenm/troll.msp|title=Troll dice roller and probability calculator}}
54. ^1 {{cite web|url=https://github.com/zz5013/pwCompiler|title=PWhile Compiler|work=GitHub}}
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}}

External links

  • List of Probabilistic Model Mini Language Toolkits
  • Probabilistic programming wiki

2 : Probabilistic models|Probabilistic software
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