Probabilistic Models of Cognition by Noah D. Goodman, Joshua B. Tenenbaum & The ProbMods Contributors



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 probabilistic programs (using the WebPPL language).

Contributors

This book is an open source project. We welcome content contributions (via GitHub)!

The ProbMods Contibutors are:

Noah D. Goodman (editor)

Joshua B. Tenenbaum

Daphna Buchsbaum

Joshua Hartshorne

Robert Hawkins

Timothy J. O’Donnell

Michael Henry Tessler



Citation

https://probmods.org/

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

Acknowledgments

N. D. Goodman, J. B. Tenenbaum, and The ProbMods Contributors (2016).(2nd ed.). Retrieved from

We are grateful for crucial technical assitance from: Andreas Stuhlmüller, Tomer Ullman, John McCoy, Long Ouyang, Julius Cheng.

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).

Previous edition

The first edition of this book used the probabilistic programming language Church and can be found here