# A Bayesian Model for Online Activity Sample Sizes

arXiv: https://arxiv.org/abs/2111.12157 · HTML: https://mlsanity.com/paper/2111.12157/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Designing and Deploying Online Field Experiments](https://mlsanity.com/paper/1409.3174/) (2014)
- [Datasets for Online Controlled Experiments](https://mlsanity.com/paper/2111.10198/) (2021)

## Built on by (most cited first, 4 of 4)

- [Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring](https://mlsanity.com/paper/2304.00420/) (2023)
- [Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches](https://mlsanity.com/paper/2312.12871/) (2023)
- [A Nonparametric Bayes Approach to Online Activity Prediction](https://mlsanity.com/paper/2401.14722/) (2024)
- [Improved prediction of future user activity in online A/B testing](https://mlsanity.com/paper/2402.03231/) (2024)

## Abstract (arXiv)

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.