kobyszcze

You could do a lot of ML without GPU support. Especially if you are dealing with sparse data (like text).

I wrote a simple example of calling out into Rust from Python using cffi: https://github.com/maciejkula/python-rustlearn.

The gist of it is: it's really easy.

You could use identical priors for each variant, with the mean equal to the empirical mean of your existing system, and the variance controlling how plausible big improvements are.

In this case your prior mean could be something as simple as an average conversion rate for all your past articles.

Remember that by using frequentist statistics your are implicitly assuming a uniform prior---I think it's quite easy to find weakly informative priors which are more plausible than saying that any conversion rate is equally likely.

Thanks for the comment. We do look at comparing proportions in two samples (have a look at our calculator: http://developers.lyst.com/bayesian-calculator/). The way we do this is by constructing a posterior distribution of differences between the two proportions through taking samples from the proportion posterior distributions and taking their difference. But yes, we should have made it more obvious in the post!

Thanks for the comment! What we were trying to get at is running repeated experiments when prior probability of an experiment being successful is low---which you correctly point out is also about repeated testing (and has nothing to do with power). So perhaps our naming is unfortunate.