# Big Data on the Rise: Testing monotonicity of distributions

arXiv: https://arxiv.org/abs/1501.06783 · HTML: https://mlsanity.com/paper/1501.06783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [Perfect Sampling from Pairwise Comparisons](https://mlsanity.com/paper/2211.12868/) (2022)
- [Testing properties of distributions in the streaming model](https://mlsanity.com/paper/2309.03245/) (2023)
- [Optimal Algorithms for Augmented Testing of Discrete Distributions](https://mlsanity.com/paper/2412.00974/) (2024)

## Abstract (arXiv)

The field of property testing of probability distributions, or distribution testing, aims to provide fast and (most likely) correct answers to questions pertaining to specific aspects of very large datasets. In this work, we consider a property of particular interest, monotonicity of distributions. We focus on the complexity of monotonicity testing across different models of access to the distributions; and obtain results in these new settings that differ significantly from the known bounds in the standard sampling model.