https://probmods.org/

  * 
      + Home
      + 
      + Introduction
      + Generative models
      + Conditioning
      + Causal and statistical dependence
      + Conditional dependence
      + Social cognition
      + Interlude - Bayesian data analysis
      + Interlude - Algorithms for inference
      + Rational process models
      + Learning as conditional inference
      + Learning with a language of thought
      + Hierarchical models
      + Occam's Razor
      + Mixture models
      + Learning (deep) continuous functions
      + Appendix - JavaScript basics
      + Appendix - Useful distributions

Probabilistic Models of Cognition

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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

N. D. Goodman, J. B. Tenenbaum, and The ProbMods Contributors (2016).
Probabilistic Models of Cognition (2nd ed.). Retrieved YYYY-MM-DD
from https://probmods.org/
[bibtex]

@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

We are grateful for crucial technical assitance from: Andreas
Stuhlmuller, 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.

Chapters

 1. Introduction
    A brief introduction to the philosophy.
 2. Generative models
    Representing working models with probabilistic programs.
 3. Conditioning
    Asking questions of models by conditional inference.
 4. Causal and statistical dependence
    Causal and statistical dependence.
 5. Conditional dependence
    Patterns of inference as evidence changes.
 6. Social cognition
    Inference about inference
 7. Interlude - Bayesian data analysis
    Making scientific inferences from data.
 8. Interlude - Algorithms for inference
    Approximate inference. Efficiency tradeoffs of different
    algorithms.
 9. Rational process models
    The psychological reality of inference algorithms.
10. Learning as conditional inference
    How inferences change as data accumulate.
11. Learning with a language of thought
    Compositional hypothesis spaces.
12. Hierarchical models
    The power of statistical abstraction.
13. Occam's Razor
    How inference penalizes extra model flexibility.
14. Mixture models
    Models for inferring the kinds of things.
15. Learning (deep) continuous functions
    Functional hypothesis spaces and deep probabilistic models
16. Appendix - JavaScript basics
    A very brief primer on JavaScript.
17. Appendix - Useful distributions
    A very brief summary of some important distributions.