Annotated reading list for getting into Bayes

This recently published paper provides an annotated reading list for learning about Bayesian modeling. From one of the authors:

We were invited to submit this paper for a special issue on Bayesian statistics for Psychonomic Bulletin and Review. Each paper in the special issue addresses a specific question we often hear about Bayesian statistics, and ours was the following:

I am a reviewer/editor handling a manuscript that uses Bayesian methods; which articles should I read to get a quick idea of what that means?

Read the rest of the blog post here (also includes a link to the paper).

CASE is offering Bayesian modeling workshops this term!

Just because Bayes Club isn’t meeting doesn’t mean you can’t get a little bit of the old Reverend and his theorem in your life this term!

The first workshop is Oct 23-24, and the second is Nov 6-7. You’ll get tons of focused instruction on how Bayesian modeling works and when/why you might want to do it, plenty of concrete examples, and a big, gorgeous pile of code to take home, which you can re-run at your leisure or just set up a giant Bayesian model code swimming pool in your mansion, a la Scrooge McDuck:


For details, including the sign-up form:


End of Term Reminder

Just a friendly reminder, since we’re approaching the end of the term. If you’re taking Bayes Club for credit, you need to:

1) attend Bayes Club (we’ve been keeping track of attendance, so you’re covered there)

2) send a one-page-ish write-up to Sanjay ( by the end of the term, describing what you’ve done and/or what you’ve learned in Bayes Club this term.

If you are signed up for credit and you think you’ll have trouble meeting one or both of these requirements, email Sanjay.

Bayes Club & | R Club

Play choose your own adventure with our very own data science club line-up! You come to a fork in the road. Do you take it? If so, turn to page round(runif(1,1,100*pi)).

Seriously, though: we have pretty much no days left to make this decision, so please respond to the below as soon as you can. It’s only a few questions long. And thank you so much for taking the time to do so — it’ll be really helpful!

A Quick Bayes’ Theorem Reference Tool in Python

UPDATE 2015-02-16: I’ve added a conceptual explanation of this code here.

Here’s a quick script that you can use (e.g., with a bash “alias” file, which sets shortcuts for the Unix-style command line) for making decisions with Bayes. It’s based off of Nate Silver’s version of Bayes’ Theorem. It can be run with python /path/to/this/

# The above line just tells the interpreter that this is a python script.

# Jacob Levernier, 2013
# This code is released CC0 ( (Public Domain)



# (See and
x=float(input('Make an estimation: How likely (out of 1.00) would you have estimated it would be for your hypothesis to be correct, before seeing this new information (The "prior")? '))

y=float(input('How likely (out of 1.00) do you think it is to see these results if your hypothesized explanation is correct? '))

z=float(input('How likely (out of 1.00) do you think it is to see these results if your hypothesized explanation is NOT correct? '))


print "The revised estimate of how likely (out of 1.00) it is to see these results if your hypothesis is correct, given current circumstances, is",posteriorProbability

Bayes by hand, the sequel!

See the original post on doing Bayes by hand for some information about what conjugate priors are and why we might care. The demonstration today is showing that the beta distribution is a conjugate prior for a binomial likelihood function. Have fun!

Rose’s notes:

R code:

desmos plot: