[Note: This course is still being developed. Any feedback would be greatly appreciated and can be sent to tessler@mit.edu]

Learning statistics is like learning pottery. With pottery, you can learn how to make different shapes (e.g. a bowl, a vase, a spoon) without understanding general principles. The other way is to learn the basic strokes of forming pottery (e.g. how to mold a curved surface, a flat surface, long pointy things). In this course, we are going to learn the basic strokes of statistics, and compose those strokes to make shapes you’ve seen before (e.g. a t-test), some shapes you’ve probably never seen before, and develop ideas how you would make new shapes if you needed to. We won’t learn what tests apply to what data types but instead foster the ability to reason through data analysis. We will do this through the lens of Bayesian statistics, though the basic ideas will aid your understanding of classical (frequentist) statistics as well.


  1. Why analyze data
    Course overview

  2. Probabilistic programming
    A brief introduction.

  3. Learning about a hypothesis
    Models formalize hypotheses

  4. Comparing hypotheses
    Hypothesis testing is model comparison

  5. Causal models
    Reasoning with structured knowledge

  6. Elaborating models
    A pinch of sophistication and elegance

  7. Inference Algorithms
    The various approximate inference algorithms WebPPL provides and the classes of programs for which they are each best suited.

  8. Analyzing Bayesian cognitive models
    The fully Bayesian treatment


  1. Coming up with priors
    Systematically interrogating one’s knowledge

  2. Bayesian inference in a probabilistic program
    Understanding observe, condition, and factor via rejection sampling


M. H. Tessler (in prep). Bayesian data analysis: An introduction using probabilistic programs. Retrieved from https://mhtess.github.io/bdappl/

Useful resources

WebPPL support and packages

Basic WebPPL tutorials

Bayesian Data Analysis (using WebPPL)

Other WebPPL applications

Great textbooks on Bayesian Data Analysis