# Heavy Hitters and Bernoulli Convolutions

arXiv: https://arxiv.org/abs/1905.08930 · HTML: https://mlsanity.com/paper/1905.08930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2019.

## Built on by (most cited first, 1 of 1)

- [Improving Recommendation Relevance by simulating User Interest](https://mlsanity.com/paper/2302.01522/) (2023)

## Abstract (arXiv)

A very simple event frequency approximation algorithm that is sensitive to event timeliness is suggested. The algorithm iteratively updates categorical click-distribution, producing (path of) a random walk on a standard n-dimensional simplex. Under certain conditions, this random walk is self-similar and corresponds to a biased Bernoulli convolution. Algorithm evaluation naturally leads to estimation of moments of biased (finite and infinite) Bernoulli convolutions.