Statistics as Pottery: Bayesian Data Analysis using Probabilistic Programs
- Michael Tessler, Psychology, Stanford University, Stanford, California, United States
- Noah Goodman, Psychology, Stanford University, Stanford, California, United States
AbstractProbability 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 statistical approaches (e.g., Null Hypothesis Significance Testing) that provides the scientist with clarity and ease to address their personal scientific questions. Doing BDA in a probabilistic programming language (PPL) affords additional advantages: a compositional approach to writing models, separation of model specification from algorithmic implementation (a la lm() in R), and continuity from articulating data analytic models to Bayesian cognitive models. 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 online web-book and tools for integrating BDA into their existing workflow.