Title

Property testing of joint distributions using conditional samples

Article Type

Research Article

Publication Title

ACM Transactions on Computation Theory

Abstract

In this article, we consider the problem of testing properties of joint distributions under the Conditional Sampling framework. In the standard sampling model, sample complexity of testing properties of joint distributions are exponential in the dimension, resulting in inefficient algorithms for practical use. While recent results achieve efficient algorithms for product distributions with significantly smaller sample complexity, no efficient algorithm is expected when the marginals are not independent. In this article, we initialize the study of conditional sampling in the multidimensional setting. We propose a subcube conditional sampling model where the tester can condition on a (adaptively) chosen subcube of the domain. Due to its simplicity, this model is potentially implementable in many practical applications, particularly when the distribution is a joint distribution over Σn for some set Σ. We present algorithms for various fundamental properties of distributions in the subcube-conditioning model and prove that the sample complexity is polynomial in the dimension n (and not exponential as in the traditional model). We present an algorithm for testing identity to a known distribution using Õ(n2)subcube-conditional samples, an algorithm for testing identity between two unknown distributions using Õ(n5)-subcube-conditional samples and an algorithm for testing identity to a product distribution using Õ(n5)-subcube-conditional samples. The central concept of our technique involves an elegant chain rule, which can be proved using basic techniques of probability theory, yet it is powerful enough to avoid the curse of dimensionality.

DOI

10.1145/3241377

Publication Date

8-1-2018

Comments

All Open Access, Green

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