{"pk":27694,"title":"Statistics as Pottery: Bayesian Data Analysis using Probabilistic Programs (Tutorial)","subtitle":null,"abstract":"Probability theory is the “logic of science” (Jaynes, 2003) and\nBayesian data analysis (BDA) is the glue that brings that logic\nto data. BDA is a general, flexible alternative to standard statis-\ntical approaches (e.g., NHST) that provides the scientist with\nclarity and ease to address their personal scientific questions.\nDoing BDA in a probabilistic programming language (PPL) af-\nfords several additional advantages: a compositional approach\nto writing models, separation of model specification from al-\ngorithmic implementation (a la lm() in R), and continuity from\narticulating data analytic models to Bayesian cognitive mod-\nels. Furthermore, specifying one’s model and data analysis\nin a PPL allows you to search for “optimal experiments” for\nfree. This tutorial will walk the participant through the basics\nof BDA to state-of-the-art applications, using an interactive on-\nline web-book and tools for integrating BDA into their existing\nworkflow.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"bayesian data analysis; bayesian cognitive modeling; probabilistic programming"}],"section":"Tutorials","is_remote":true,"remote_url":"https://escholarship.org/uc/item/8hn1k8qp","frozenauthors":[{"first_name":"Michael","middle_name":"Henry","last_name":"Tessler","name_suffix":"","institution":"Stanford University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27694/galley/17335/download/"}]}