Big data on the rise? Testing monotonicity of distributions

Abstract

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 [5, 7, 8, 20]; and obtain results in these new settings that differ significantly (and somewhat surprisingly) from the known bounds in the standard sampling model [1].