How to enhance AB testing with the Multi-arm Bandit

An alternative algorithm for testing that allows for a more rapid decision than traditional apporaches.
Data-Viz
R
TidyTuesday
Shiny
Bayesian
Author

Rahim Jutha

Published

September 5, 2023

Message to get across “Multi-arm bandits offer an approach to testing, especially web testing, that allows explicit optimization and more rapid decision making than the traditional statistical approach to designing experiments” (Peter Bruce 2020)

CONTENTS

Introduction

introduce post and what I’m writing about (an alternative to ab testing that is very applicable to web testing) providing an example for each approach along the way

AB testing

Introduce very briefly and provide a simple example. Be careful to not to spend too much time in this section since its not the main topic and focus.

Multi-Arm-Bandito

introduce algorithm formally why its better in web testing context simple example (using the psilon-greedy algorithm for an A/B test described in teh text)

Thompson’s sampling uses a Bayesian approach:

same concept using a beta prior instead seems better will read about it and provide a breif example

References

Peter Bruce, Peter Gedeck, Andrew Bruce. 2020. “Practical Statistics for Data Scientists, 2nd Edition.” https://learning.oreilly.com/library/view/practical-statistics-for/9781492072935/.

Citation

BibTeX citation:
@online{jutha2023,
  author = {Rahim Jutha},
  title = {How to Enhance {AB} Testing with the {Multi-arm} {Bandit}},
  date = {2023-09-05},
  url = {https://rjutha.github.io/posts/2023-08-15-Spam/},
  langid = {en}
}
For attribution, please cite this work as:
Rahim Jutha. 2023. “How to Enhance AB Testing with the Multi-Arm Bandit.” September 5, 2023. https://rjutha.github.io/posts/2023-08-15-Spam/.