Bayesian Data Analysis using Probabilistic Programs (CogSci 2018)

  1. Why analyze data
    Course overview

  2. Probabilistic programming
    A brief introduction.

  3. Building models
    Describing the generative process of data

  4. BDA Fundamentals
    The complete nuts and bolts

  5. Elaborating models
    A pinch of sophistication and elegance

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

  7. Analyzing Bayesian cognitive models
    The fully Bayesian treatment