<h3>Reading list</h3>

<p>As @Jeff has mentioned Tom Griffiths has several useful resources. In particular Tom Griffiths has an <a href="http://cocosci.berkeley.edu/tom/bayes.html">extensive reading list</a> that you might find relevant. To quote the summary of the content:</p>

<blockquote>
  <p>This list is intended to introduce some of the tools of Bayesian
  statistics and machine learning that can be useful to computational
  research in cognitive science. The first section mentions several
  useful general references, and the others provide supplementary
  readings on specific topics...</p>
</blockquote>

<h3>Other comments</h3>

<ul>
<li>More broadly, it certainly helps when learning Bayesian statistics to have a good understanding of calculus (integration is key) and probability 
(distributions, how they are parameterised, etc.). </li>
<li>John Kruschke's book is quite accessible </li>
<li><a href="http://www.stat.columbia.edu/~gelman/arm/">Gelman and Hill</a> is also quite accessible; the focus is multilevel modelling and regression; it covers Bayesian modelling with BUGS. It is very readable and has lots of practical advice. That said, it's doesn't specifically address issues related to cognitive science.</li>
</ul>

CC BY-SA 3.0
2012-04-19T13:55:34.270Z
Thanks - great list of resources on your blog by the way! I would also add matrix algebra to pre-requirements for learning Bayesian stats. Regarding "basic mathematics", [this book by Scott Lynch](http://www.springer.com/social+sciences/book/978-0-387-71264-2) is also nice introductory source. In the appendix there is a compact overview of calculus and matrix algebra - a nice addition that many books on the topic lacks.
Geek On Acid
CC BY-SA 3.0
2012-04-18T01:27:15.823Z
2012-04-18T05:15:22.123Z
2012-04-18T05:15:22.123Z
Jeromy Anglim
<p>In the fairly recent book <a href="http://www.cambridge.org/gb/knowledge/isbn/item1162573/?site_locale=en_GB" rel="nofollow">"The Cambridge Handbook of Computational Psychology"</a>, chapter three is devoted to bayesian modeling. It's written by Thomas Griffiths, Charles Kemp, and Joshua Tenenbaum.</p>

<p>I haven't read this chapter yet myself but will update this answer when I have.</p>

CC BY-SA 3.0
2012-04-15T12:44:39.253Z
2012-04-15T12:44:39.253Z
Speldosa
<p>+1 to Speldosa's suggestion. Griffiths and colleagues have written several primers on the use of Bayesian  models in cogsci. Many of them can be found on Griffiths' website under 'Foundations':</p>

<p><a href="http://cocosci.berkeley.edu/publications.php?topic=Foundations">http://cocosci.berkeley.edu/publications.php?topic=Foundations</a></p>

<p>e.g.</p>

<blockquote>
  <p>Perfors, A., Tenenbaum, J.B., Griffiths, T. L., &amp; Xu, F. (2011). A
  tutorial introduction to Bayesian models of cognitive development.
  Cognition, 120, 302-321.</p>
  
  <p>Griffiths, T. L., &amp; Yuille, A. (2008). A primer on probabilistic
  inference. In M. Oaksford and N. Chater (Eds.). The probabilistic
  mind: Prospects for rational models of cognition. Oxford: Oxford
  University Press.</p>
</blockquote>

CC BY-SA 3.0
2012-04-17T23:50:58.350Z
2012-04-17T23:50:58.350Z
Jeff
<p>On <a href="https://stats.stackexchange.com/">Cross Validated</a> there is a great question about <a href="https://stats.stackexchange.com/questions/125/what-is-the-best-introductory-bayesian-statistics-textbook">best introductory books for bayesian statistics</a>. Also, Jeromy Anglim <a href="http://jeromyanglim.blogspot.com/2012/04/getting-started-with-jags-rjags-and.html" rel="nofollow noreferrer">blogged recently about use of JAGS, rjags, and Bayesian Modelling</a>, with some very nice collection of tutorials relevant to the above question. Lots of those resources are single-shot tutorials, covering just some limited scope of programming and modelling. </p>

<p>In terms of resources that cover a broader range of topics with some background information and coding tutorials, only two sources stand out from the list:</p>

<ul>
<li><p>Michael Lee and Eric Wagenmakers <a href="http://www.ejwagenmakers.com/BayesCourse/BayesBook.html" rel="nofollow noreferrer">Course in Bayesian Graphical Modeling for Cognitive Science</a>, and </p>

<ul>
<li>free and aimed at the beginners, but it's a bit rough (looks like a draft, which authors kind of confirm on their website).</li>
</ul></li>
<li><p>John Kruschke <a href="http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/" rel="nofollow noreferrer">Doing Bayesian Data Analysis: A Tutorial with R and BUGS</a>.  </p>

<ul>
<li>I can't check (cause' someone stole it from my library), but opinions about it are highly positive.</li>
</ul></li>
</ul>

<p>Those two books could potentially hit the spot in terms of sufficient coverage of basic needs for beginner bayesian acolyte. </p>

<p><strong>What else do you advise as a simple, practical, compact, and thorough introduction to bayesian modeling for a cognitive scientist?</strong></p>

<cognitive-modeling><reference-request><mathematical-psychology><statistics><bayesian>
Introductory resources on bayesian modeling for cognitive sciences
Geek On Acid
# Introductory resources on bayesian modeling for cognitive sciences

(Asked by: username_0 on 2012-04-13)

On [Cross Validated](https://stats.stackexchange.com/) there is a great question about [best introductory books for bayesian statistics](https://stats.stackexchange.com/questions/125/what-is-the-best-introductory-bayesian-statistics-textbook). Also, username_1 [blogged recently about use of JAGS, rjags, and Bayesian Modelling](http://jeromyanglim.blogspot.com/2012/04/getting-started-with-jags-rjags-and.html), with some very nice collection of tutorials relevant to the above question. Lots of those resources are single-shot tutorials, covering just some limited scope of programming and modelling.

In terms of resources that cover a broader range of topics with some background information and coding tutorials, only two sources stand out from the list:

* Michael Lee and Eric Wagenmakers [Course in Bayesian Graphical Modeling for Cognitive Science](http://www.ejwagenmakers.com/BayesCourse/BayesBook.html), and

  + free and aimed at the beginners, but it's a bit rough (looks like a draft, which authors kind of confirm on their website).
* John Kruschke [Doing Bayesian Data Analysis: A Tutorial with R and BUGS](http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/).

  + I can't check (cause' someone stole it from my library), but opinions about it are highly positive.

Those two books could potentially hit the spot in terms of sufficient coverage of basic needs for beginner bayesian acolyte.

**What else do you advise as a simple, practical, compact, and thorough introduction to bayesian modeling for a cognitive scientist?**

**Tags:** cognitive-modeling, reference-request, mathematical-psychology, statistics, bayesian


---

## 3 answers

### Answer by username_3 on 2012-04-17. Score: 6 | Accepted answer

+1 to username_2's suggestion. Griffiths and colleagues have written several primers on the use of Bayesian models in cogsci. Many of them can be found on Griffiths' website under 'Foundations':

<http://cocosci.berkeley.edu/publications.php?topic=Foundations>

e.g.

> Perfors, A., Tenenbaum, J.B., Griffiths, T. L., & Xu, F. (2011). A
> tutorial introduction to Bayesian models of cognitive development.
> Cognition, 120, 302-321.
>
> Griffiths, T. L., & Yuille, A. (2008). A primer on probabilistic
> inference. In M. Oaksford and N. Chater (Eds.). The probabilistic
> mind: Prospects for rational models of cognition. Oxford: Oxford
> University Press.


---

### Answer by username_1 on 2012-04-18. Score: 5

### Reading list

As @username_3 has mentioned Tom Griffiths has several useful resources. In particular Tom Griffiths has an [extensive reading list](http://cocosci.berkeley.edu/tom/bayes.html) that you might find relevant. To quote the summary of the content:

> This list is intended to introduce some of the tools of Bayesian
> statistics and machine learning that can be useful to computational
> research in cognitive science. The first section mentions several
> useful general references, and the others provide supplementary
> readings on specific topics...

### Other comments

* More broadly, it certainly helps when learning Bayesian statistics to have a good understanding of calculus (integration is key) and probability
  (distributions, how they are parameterised, etc.).
* John Kruschke's book is quite accessible
* [Gelman and Hill](http://www.stat.columbia.edu/~gelman/arm/) is also quite accessible; the focus is multilevel modelling and regression; it covers Bayesian modelling with BUGS. It is very readable and has lots of practical advice. That said, it's doesn't specifically address issues related to cognitive science.

> **Comments:**
> **username_0**: Thanks - great list of resources on your blog by the way! I would also add matrix algebra to pre-requirements for learning Bayesian stats. Regarding "basic mathematics", [this book by Scott Lynch](http://www.springer.com/social+sciences/book/978-0-387-71264-2) is also nice introductory source. In the appendix there is a compact overview of calculus and matrix algebra - a nice addition that many books on the topic lacks.  

---

### Answer by username_2 on 2012-04-15. Score: 3

In the fairly recent book ["The Cambridge Handbook of Computational Psychology"](http://www.cambridge.org/gb/knowledge/isbn/item1162573/?site_locale=en_GB), chapter three is devoted to bayesian modeling. It's written by Thomas Griffiths, Charles Kemp, and Joshua Tenenbaum.

I haven't read this chapter yet myself but will update this answer when I have.


---

