Resources to support the implementation and testing of probabilistic models and inference methods, including an interactive electronic text with an embedded programming environment, a web-based probabilistic programming language, working model implementations, and sample datasets.
- Goodman, N. D. & Tenenbaum, J. B. (2016) Probabilistic Models of Cognition (2nd edition, electronic)
- To learn more about how WebPPL works, see the web book, The Design and Implementation of Probabilistic Programming Languages by N.D. Goodman and A. Stuhlmuller.
- The interactive web book, Modeling Agents with Probabilistic Programs, by O. Evans, A. Stuhlmuller, J. Salvatier, and D. Filan, describes and implements models of rational agents for Partially Observable Markov Decision Processes ((PO)MDPs) and Reinforcement Learning. This framework enables the creation of richer models of human planning that capture human biases and bounded rationality.
- The first edition of Probabilistic Models of Cognition used the Church probabilistic programming language. Sample models implemented in Church can be found at the forestdb.org website. Also see the Church language tutorial by Tomer Ullman.
- The unit on Modeling Human Cognition in the Brains, Minds and Machines Summer Course on MIT OpenCourseWare includes lecture videos and slides by Josh Tenenbaum and resources for further study.
- Three-part lecture by Josh Tenenbaum on Computational Models of Cognition (Part 1, Part 2, Part 3)
- Computational tutorial on Bayesian methods: Brain & Cognitive Perspectives by Mehrdad Jazayeri and Josh Tenenbaum.
- Computational tutorial on Bayesian Inference in Generative Models by Luke Hewitt
- Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. (2015) Human-level concept learning through probabilistic program induction, Science, 350(6266):1332-1338. Accompanied omniglot dataset.