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Actually, in the frequentist paradigm you could choose to run a sequential hypothesis test which will end when you've acquired sufficient data[1]. Or, if you wa

by nickhuh 10y ago

Actually, in the frequentist paradigm you could choose to run a sequential hypothesis test which will end when you've acquired sufficient data[1]. Or, if you want to get fancy you could use a multi-armed bandit approach which is probably optimal in many situations in perhaps a more robust way than many Bayesian methods[2]. Really both can work well. My advice is, use whichever you know well enough to utilize effectively! [1]: https://en.m.wikipedia.org/wiki/Sequential_analysis https://en.m.wikipedia.org/wiki/Sequential_analysis [2]: https://en.m.wikipedia.org/wiki/Multi-armed_bandit https://en.m.wikipedia.org/wiki/Multi-armed_bandit

kgwgk 10y ago
> Actually, in the frequentist paradigm you could choose to run a sequential hypothesis test which will end when you've acquired sufficient data And, as he said, you have to make adjustments to account for these interim analysis.
paulddraper 10y ago
Right, as I said, it can be done with frequentist statistics. This is what Optimizely does (http://pages.optimizely.com/rs/optimizely/images/stats_engine_technical_paper.pdf http://pages.optimizely.com/rs/optimizely/images/stats_engin...). But (1) it is not simple and (2) it is not optimal. --- Agreed 100% about multi-armed bandit which is what I was referring to. And the canonical solutions are in fact Bayesian :) See the Google Analytics link or lookup "Thompson sampling" From your Wikipedia link: "Probability matching strategies are also known as Thompson sampling or Bayesian Bandits, and surprisingly easy to implement if you can sample from the posterior for the mean value of each alternative."
- nickhuh 10y ago
  Oh yeah there are a few Bayesian methods which work great, Bayes-UCB is another. Personally though I think KL-UCB or just plain old UCB would be the ones I'd choose. Like I said earlier, I think these techniques are like programming languages: choose the one you know well enough to get the job done with it.

by nickhuh 10y ago

kgwgk 10y ago

> Actually, in the frequentist paradigm you could choose to run a sequential hypothesis test which will end when you've acquired sufficient data And, as he said, you have to make adjustments to account for these interim analysis.

paulddraper 10y ago

Right, as I said, it can be done with frequentist statistics. This is what Optimizely does (http://pages.optimizely.com/rs/optimizely/images/stats_engine_technical_paper.pdf http://pages.optimizely.com/rs/optimizely/images/stats_engin...). But (1) it is not simple and (2) it is not optimal. --- Agreed 100% about multi-armed bandit which is what I was referring to. And the canonical solutions are in fact Bayesian :) See the Google Analytics link or lookup "Thompson sampling" From your Wikipedia link: "Probability matching strategies are also known as Thompson sampling or Bayesian Bandits, and surprisingly easy to implement if you can sample from the posterior for the mean value of each alternative."

nickhuh 10y ago
Oh yeah there are a few Bayesian methods which work great, Bayes-UCB is another. Personally though I think KL-UCB or just plain old UCB would be the ones I'd choose. Like I said earlier, I think these techniques are like programming languages: choose the one you know well enough to get the job done with it.