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arXiv · 1612.03156

Testing Bayesian Networks

Clement Canonne·Ilias Diakonikolas·Daniel Kane·Alistair Stewart
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Abstract

This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node. The value at any particular node is conditionally independent of all the other non-descendant nodes once its parents are fixed. Specifically, we study the properties of identity testing and closeness testing of Bayesian networks. Our main contribution is the first non-trivial efficient testing algorithms for these problems and corresponding information-theoretic lower bounds. For a wide range of parameter settings, our testing algorithms have sample complexity sublinear in the dimension and are sample-optimal, up to constant factors.

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BibTeXRIS

Clement Canonne, Ilias Diakonikolas, Daniel Kane, Alistair Stewart. 2016-12-09. Testing Bayesian Networks. https://arxiv.org/abs/1612.03156

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