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webppl probabilistic programming for the web on github web ppl probabilistic programming for the web webppl is a feature rich probabilistic programming language embedded in javascript check out some demos or try it yourself in the editor below features runs on the command line with node js or in the browser supports modular and re usable code using packages built on top of the npm package system and interoperates with existing javascript packages in the npm ecosystem includes a large and expanding library of primitive distributions implements a variety of inference algorithms including exact inference via enumeration rejection sampling sequential monte carlo markov chain monte carlo hamiltonian monte carlo and inference as optimization e g variational inference provides inference as a first class operator in the language allowing for nested inference inference about inference supports optimizable models with neural network components using adnn demos browser based applications powered by webppl procedural vines with shape constraints 3d procedural spaceships with shape constraints note the code in this demo is written in an older version of webppl local install install webppl in two easy steps install node js run npm install g webppl now the webppl command is globally available to upgrade to the latest version run npm update g webppl documentation to learn more about how to set up and use webppl take a look at our documentation and the examples to learn more about how webppl works under the hood check out our web book the design and implementation of probabilistic programming languages for probabilistic modeling in general our other web book probabilistic models of cognition might be of interest license the webppl code base is open source and freely available for commerical and non commercial use under the mit license contributions we encourage you to contribute to webppl check out our guidelines for contributors and join the webppl dev mailing list pronunciation say web people citing if you use webppl in academic projects and papers please cite as n d goodman and a stuhlmüller electronic the design and implementation of probabilistic programming languages retrieved from http dippl org bibtex misc dippl title the design and implementation of probabilistic programming languages author goodman noah d and stuhlm u ller andreas year 2014 howpublished url http dippl org note accessed publications if you publish a paper using extending webppl let us know and we ll add it to this list d ritchie p horsfall and n d goodman deep amortized inference for probabilistic programs arxiv 1610 05735 l ouyang m h tessler d ly and n d goodman practical optimal experiment design with probabilistic programs arxiv 1608 05046 m h tessler and n d goodman a pragmatic theory of generic language arxiv 1608 02926 d ritchie a thomas p hanrahan and n d goodman neurally guided procedural models amortized inference for procedural graphics programs using neural networks nips 2016 d ritchie a stuhlmüller and n d goodman c3 lightweight incrementalized mcmc for probabilistic programs using continuations and callsite caching aistats 2016 m h tessler and n d goodman communicating generalizations about events proceedings of the thirty eighth annual conference of the cognitive science society 2016 e j yoon m h tessler n d goodman and m c frank talking with tact polite language as a balance between kindness and informativity proceedings of the thirty eighth annual conference of the cognitive science society 2016 c graf j degen r x d hawkins and n d goodman animal dog or dalmatian level of abstraction in nominal referring expressions proceedings of the thirty eighth annual conference of the cognitive science society 2016 o evans a stuhlmüller and n d goodman learning the preferences of ignorant inconsistent agents aaai 2016 a stuhlmüller r x d hawkins n siddharth and n d goodman coarse to fine sequential monte carlo for probabilistic programs arxiv 1509 02962 o evans a stuhlmüller and n d goodman learning the preferences of bounded agents workshop on bounded optimality nips 2015 r x d hawkins a stuhlmüller j degen and n d goodman why do you ask good questions provoke informative answers proceedings of the thirty seventh annual conference of the cognitive science society 2015 g scontras and m h tessler electronic composition in probabilistic language understanding retrieved from http gscontras github io esslli 2016 o evans a stuhlmüller j salvatier and d filan electronic modeling agents with probabilistic programs retrieved from http agentmodels org n d goodman and j b tenenbaum electronic probabilistic models of cognition retrieved from http probmods org n d goodman and a stuhlmüller electronic the design and implementation of probabilistic programming languages retrieved from http dippl org acknowledgments the webppl project is supported by grants from darpa under agreement number fa8750 14 2 0009 and the office of naval research grant number n00014 13 1 0788 webppl is a stanford cocolab project