Hi all, I’m using pymc3 to do some Bayesian A/B testing. The beauty of this approach – in contrast to traditional frequentist approaches – is that there are a variety of metrics you can generate and learn a hell of a lot more about our experiment. I’m most interested in computing the error metrics…

The probability that \lambda_B > \lambda_A (section 6.1) can be calculated (approximately) directly: pBgtA = (trace.posterior['Control'] > trace.posterior['Optimisation']).mean() The expected loss with would be something like this: upliftLossA = (trace.posterior['Control'] - trace.posterior['Opti…

Creating custom Joint Distribution in pymc3

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jules May 12, 2022, 10:31am 5

Thanks!

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Creating custom Joint Distribution in pymc3

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