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@article{Teymur:2020:10.3934/fods.2020009,
author = {Teymur, O and Filippi, S},
doi = {10.3934/fods.2020009},
journal = {Foundations of Data Science},
pages = {155--172},
title = {A Bayesian nonparametric test for conditional independence},
url = {http://dx.doi.org/10.3934/fods.2020009},
volume = {2},
year = {2020}
}

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TY  - JOUR
AB  - 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.
AU  - Teymur,O
AU  - Filippi,S
DO  - 10.3934/fods.2020009
EP  - 172
PY  - 2020///
SN  - 2639-8001
SP  - 155
TI  - A Bayesian nonparametric test for conditional independence
T2  - Foundations of Data Science
UR  - http://dx.doi.org/10.3934/fods.2020009
UR  - http://arxiv.org/abs/1910.11219v2
UR  - http://hdl.handle.net/10044/1/81568
VL  - 2
ER  -

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