This book explores the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models.
We examine how a broad range of empirical phenomena, including intuitive physics, concept learning, causal reasoning, social cognition, and language understanding, can be modeled using a functional probabilistic programming language called WebPPL.

`http://probmods.org/v2`

[bibtex]

@misc{probmods2, title = {{Probabilistic Models of Cognition}}, edition = {Second}, author = {Goodman, Noah D and Joshua B. Tenenbaum}, year = {2016}, howpublished = {\url{http://probmods.org/v2}}, note = {Accessed: } }

- Book content

*Markdown code for the book chapters* - WebPPL

*A probabilistic programming language for the web*

We are grateful to the following people, who contributed content or technical expertise: Timothy J. Oâ€™Donnell, Andreas Stuhlmuller, Tomer Ullman, John McCoy, Long Ouyang, Julius Cheng, and Robert Hawkins.

The construction and ongoing support of this tutorial are made possible by grants from the Office of Naval Research, the James S. McDonnell Foundation, the Stanford VPOL, and the Center for Brains, Minds, and Machines (funded by NSF STC award CCF-1231216).

- Introduction

*A brief introduction to the philosophy.* - Generative models

*Representing working models with probabilistic programs.* - Conditioning

*Asking questions of models by conditional inference.* - Patterns of inference

*Causal and statistical dependence. Conditional dependence.* - Models for sequences of observations

*Generative models of the relations between data points* - Inference about inference

*Models on models on models* - Algorithms for inference

*From competence to process, efficiency tradeoffs of different algorithms.* - Learning as conditional inference

*How inferences change as data accumulate.* - Hierarchical models

*The power of abstraction.* - Occam's Razor

*Penalizing extra model flexibility.* - Mixture models

*Models for inferring the kinds of things.* - Non-parametric models

*What to do when you don't know how many kinds there are.* - Appendix - JavaScript basics

*A very brief primer on JavaScript.* - Bayesian data analysis

*Making scientific inferences about data and models*