A Bayesian Model for Online Activity Sample Sizes

Thomas S. Richardson, Yu Liu, James Mcqueen, Doug Hains
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:1775-1785, 2022.

Abstract

In many contexts it is useful to predict the number of individuals in some population who will initiate a particular activity during a given period. For example, the number of users who will install a software update, the number of customers who will use a new feature on a website or who will participate in an A/B test. In practical settings, there is heterogeneity amongst individuals with regard to the distribution of time until they will initiate. For these reasons it is inappropriate to assume that the number of new individuals observed on successive days will be identically distributed. Given observations on the number of unique users participating in an initial period, we present a simple but novel Bayesian method for predicting the number of additional individuals who will participate during a subsequent period. We illustrate the performance of the method in predicting sample size in online experimentation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-richardson22a, title = { A Bayesian Model for Online Activity Sample Sizes }, author = {Richardson, Thomas S. and Liu, Yu and Mcqueen, James and Hains, Doug}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {1775--1785}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/richardson22a/richardson22a.pdf}, url = {https://proceedings.mlr.press/v151/richardson22a.html}, abstract = { In many contexts it is useful to predict the number of individuals in some population who will initiate a particular activity during a given period. For example, the number of users who will install a software update, the number of customers who will use a new feature on a website or who will participate in an A/B test. In practical settings, there is heterogeneity amongst individuals with regard to the distribution of time until they will initiate. For these reasons it is inappropriate to assume that the number of new individuals observed on successive days will be identically distributed. Given observations on the number of unique users participating in an initial period, we present a simple but novel Bayesian method for predicting the number of additional individuals who will participate during a subsequent period. We illustrate the performance of the method in predicting sample size in online experimentation. } }
Endnote
%0 Conference Paper %T A Bayesian Model for Online Activity Sample Sizes %A Thomas S. Richardson %A Yu Liu %A James Mcqueen %A Doug Hains %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-richardson22a %I PMLR %P 1775--1785 %U https://proceedings.mlr.press/v151/richardson22a.html %V 151 %X In many contexts it is useful to predict the number of individuals in some population who will initiate a particular activity during a given period. For example, the number of users who will install a software update, the number of customers who will use a new feature on a website or who will participate in an A/B test. In practical settings, there is heterogeneity amongst individuals with regard to the distribution of time until they will initiate. For these reasons it is inappropriate to assume that the number of new individuals observed on successive days will be identically distributed. Given observations on the number of unique users participating in an initial period, we present a simple but novel Bayesian method for predicting the number of additional individuals who will participate during a subsequent period. We illustrate the performance of the method in predicting sample size in online experimentation.
APA
Richardson, T.S., Liu, Y., Mcqueen, J. & Hains, D.. (2022). A Bayesian Model for Online Activity Sample Sizes . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:1775-1785 Available from https://proceedings.mlr.press/v151/richardson22a.html.

Related Material