In their online book Probabilistic Models of Cognition, Goodman and Tenenbaum present a wide range of probabilistic cognitive models. The goal of this tutorial is to implement some of the hierarchical Bayesian models from that book.
Important note: The changes you make to the code are lost once you refresh the page. If you want save your code, copy-paste to some text editor. This should not be a problem as you don’t have to code much.
Tip: If you want to play around with WebPPL, you can find an editor and more examples on webppl.org.
Submission. Submit your solutions to the homework exercises a blackboard before the lecture on Thursday (15:00). Please hand in a single PDF file in which you explain your solutions. Also, make sure your answers are easy to find and not hidden between blocks of code!
Since we have only two hours for the actual tutorial, we need to get through the basics quickly. That will work best if you come prepared, even though the preparations shouldn’t take a lot of extra time.
Probabilistic models are often best explained in simple scenarios. In this case, a classic one: bags with coloured marbles. In the first part of the tutorial we will be solely concerned with bags and marbles. Starting from the very basics of probability theory we quickly work towards a hierarchical model. The second part deals with its cognitive interpretation.