

Bayesian Analysis of Normal Distributions with Python - sergeyfeldman
http://engineering.richrelevance.com/bayesian-analysis-of-normal-distributions-with-python/

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noelwelsh
Interesting that they chose this approach. At Myna we normally squish data
through the logistic function to rescale (if necessary) and then use a beta
model. The beta model expects binary observations but you either interpret
fractional observations as the probability of a weighted coin flip, or just do
fractional updates to the beta. Both worked about the same in my tests. (This
difference is mainly due to our different approaches A/B testing. Both are
fairly textbook.)

The more interesting question, to me, is how you get credible intervals. I'm
assuming you just MCMC 'em up, but it would be nice if there was a more
computationally efficient trick.

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sergeyfeldman
We don't actually use Gaussian models for any of our Bayesian tests =). For
continuous data, we use log-normal models, and the next blog post will be
about how to make all that work.

And for intervals: yup, we just use sampling. But it's super fast because all
you need are independent samples. For more details, see this first post:

[http://engineering.richrelevance.com/bayesian-ab-
tests/](http://engineering.richrelevance.com/bayesian-ab-tests/)

