A demo of a work-in-progress tool for exploring the performance of "Bayesian Bandits" for solving the Multi-Armed Bandit problem
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AB Testing for websites
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Scoop.it!
This article describes the statistical engine behind Google Analytics Content Experiments. Google Analytics uses a multi-armed bandit approach to managing online experiments. 
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Scoop.it!
A/B testing is used far too often, for something that performs so badly. It is defective by design: Segment users into two groups. Show the A group the old, tried and true stuff. Show the B group the new whiz-bang design with the bigger buttons and slightly different copy. After a while, take a look at the stats and figure out which group presses the button more often. Sounds good, right? The problem is staring you in the face. It is the same dilemma faced by researchers administering drug studies. During drug trials, you can only give half the patients the life saving treatment. The others get sugar water. If the treatment works, group B lost out. This sacrifice is made to get good data. But it doesn't have to be this way.
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