Bandit Algorithms

Tanner Thompson describes usage of a multi-armed bandit algorithm to drive conversions:

The functional idea behind a bandit algorithm is that you make an informed decision every time you assign a visitor to a test arm. Several bandit-type algorithms have been proved to be mathematically optimal; that is, they obtain the maximum future revenue given the data they have at any given point. Gittins indexing is perhaps the foremost of these algorithms. However, the trade-off of these methods is that they tend to be very computationally intensive.

This article doesn’t show any code, but it is useful for thinking about the problem.

Related Posts

Python versus R (Again)

Alex Woodie looks at whether Python is dominating R in the data science space: There is some evidence that Python’s popularity is hurting R usage. According to the TIOBE Index, Python is currently the third most popular language in the world, behind perennial heavyweights Java and C. From August 2018 to August 2019, Python usage surged […]

Read More

Z-Tests vs T-Tests

Stephanie Glen has a picture which explains the difference between a Z-test and a T-test: The following picture shows the differences between the Z Test and T Test. Not sure which one to use? Find out more here: T-Score vs. Z-Score. Click through for the picture.

Read More

Categories

December 2016
MTWTFSS
« Nov Jan »
 1234
567891011
12131415161718
19202122232425
262728293031