(This article was originally published at Probably Overthinking It, and syndicated at StatsBlogs.)

*Think Bayes*. As always, I have been posting drafts as I go along, so you can read the current version at thinkbayes.com.

I am teaching Computational Bayesian Statistics in the spring, using the draft edition of the book. The students will work on case studies, some of which will be included in the book. And then I hope the book will be published as part of the

*Think X*series (for all

*X*). At least, that's the plan.

In the next couple of weeks, students will be looking for ideas for case studies. An ideal project has at least some of these characteristics:

- An interesting real-world application (preferably not a toy problem).
- Data that is either public or can be made available for use in the case study.
- Permission to publish the case study!
- A problem that lends itself to Bayesian analysis, in particular if there is a practical advantage to generating a posterior distribution rather than a point or interval estimate.

Examples in the book include:

- The hockey problem: estimating the rate of goals scored by two hockey teams in order to predict the outcome of a seven-game series.
- The paintball problem, a version of the lighthouse problem. This one verges on being a toy problem, but recasting it in the context of paintball got it over the bar for me.
- The kidney problem. This one is as real as it gets -- it was prompted by a question posted by a cancer patient who needed a statistical estimate of when a tumor formed.
- The unseen species problem: a nice Bayesian solution to a standard problem in ecology.

So far I have a couple of ideas prompted by questions on Reddit:

But I would love to get more ideas. If you have a problem you would like to contribute, let me know!

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