Proceedings of the Annual Meeting of the Cognitive Science Society

Probability theory is the “logic of science” (Jaynes, 2003) and Bayesian data analysis (BDA) is the glue that brings that logic to data. BDA is a general, flexible alternative to standard statis- tical approaches (e.g., NHST) that provides the scientist with clarity and ease to address their personal scientific questions. Doing BDA in a probabilistic programming language (PPL) af- fords several additional advantages: a compositional approach to writing models, separation of model specification from al- gorithmic implementation (a la lm() in R), and continuity from articulating data analytic models to Bayesian cognitive mod- els. Furthermore, specifying one’s model and data analysis in a PPL allows you to search for “optimal experiments” for free. This tutorial will walk the participant through the basics of BDA to state-of-the-art applications, using an interactive on- line web-book and tools for integrating BDA into their existing workflow.