Posts from Reliable ML

Bayes in the Wild

I talk about the Bayesian approach to wide range of problems. Show how it is related to traditional methods in ML and what tasks benefit from an alternative view.


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Bayesian AB Testing

I talk about the Bayesian approach to AB testing. The approach consists of 3 steps: making a hypothesis about the experiment, understanding time and data constraints, and interpreting the results after collecting the data. The advantage is that the Bayesian AB(C) test does not require p-values, corrections or bootstrap procedures, is conservative (does not exaggerate the result on small data) and is easily interpreted for business.


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