
Event frequency analysis without arbitrary windows - darkxanthos
http://databozo.posthaven.com/deep-in-the-weeds-event-frequency-analysis-without-arbitrary-windows
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GFK_of_xmaspast
The only "Bayes" I can see is the author starts with a uniform prior and
applies Bayes rule in one of the examples, and in fact the whole thing is kind
of all over the place.

His approach of dragging a cut point across the data and asking "is it
significant here? how about here?" is going to be prone to all sorts of false
positives: if you set your threshold to 0.05, you'll expect one positive every
20 looks at the data. (He's looking for overlaps of credible intervals; this
is going to work out to be the same thing).

Here are a couple useful links for doing this kind of problem:
[https://qualityandinnovation.com/2015/07/14/a-simple-
intro-t...](https://qualityandinnovation.com/2015/07/14/a-simple-intro-to-
bayesian-change-point-analysis/)
[http://arxiv.org/abs/0710.3742](http://arxiv.org/abs/0710.3742)

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darkxanthos
Thanks for commenting! The whole technique is foundational Bayes. Doesn't use
MCMC, but still bayes even with the use of credibility intervals. I don't make
much of a use of priors to your point and that can definitely be seen as
somewhat controversial, but each window is aggregating information on all of
the hypotheses given all of the data. That's why I qualify it as "bayesian".
How do you typically qualify bayesian vs not?

To your point of the issue with false positives... yep! :) It's a very
simplistic naive approach. I read over the article you linked to and I think
the results are fairly similar except that my (again very simplistic)
technique finds many change points if they exist... and possibly even none. So
what I'm doing generalizes more.

The sequential nature of what I'm doing bothers me too, so I'd love pointers
to other articles doing something similar.

