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A Bayesian nonparametric test for conditional independence

Teymur, Onur, Filippi, Sarah (2020) A Bayesian nonparametric test for conditional independence. Foundations of Data Science, 2 (2). pp. 155-172. ISSN 2639-8001. (doi:10.3934/fods.2020009) (KAR id:90450)

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Official URL
https://doi.org/10.3934/fods.2020009

Abstract

This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional on a third. The approach uses Pólya tree priors on spaces of conditional probability densities, accounting for uncertainty in the form of the underlying distributions in a nonparametric way. The Bayesian perspective provides an inherently symmetric probability measure of conditional dependence or independence, a feature particularly advantageous in causal discovery and not employed in existing procedures of this type.

Item Type: Article
DOI/Identification number: 10.3934/fods.2020009
Uncontrolled keywords: Bayesian nonparametrics; conditional independence testing.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Onur Teymur
Date Deposited: 29 Sep 2021 08:42 UTC
Last Modified: 07 Oct 2021 14:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90450 (The current URI for this page, for reference purposes)
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