Imperial College London
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DrSethFlaxman

Faculty of Natural Sciences, Department of Mathematics

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522Huxley BuildingSouth Kensington Campus

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Publications

Citation

BibTex format

@article{Zhang:2022:10.1080/10618600.2022.2067547,
author = {Zhang, Q and Wild, V and Filippi, S and Flaxman, S and Sejdinovic, D},
doi = {10.1080/10618600.2022.2067547},
journal = {Journal of Computational and Graphical Statistics},
pages = {1164--1176},
title = {Bayesian kernel two-sample testing},
url = {http://dx.doi.org/10.1080/10618600.2022.2067547},
volume = {31},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In modern data analysis, nonparametric measures of discrepancies between random variables are particularly important. The subject is well-studied in the frequentist literature, while the development in the Bayesian setting is limited where applications are often restricted to univariate cases. Here, we propose a Bayesian kernel two-sample testing procedure based on modeling the difference between kernel mean embeddings in the reproducing kernel Hilbert space using the framework established by Flaxman et al. The use of kernel methods enables its application to random variables in generic domains beyond the multivariate Euclidean spaces. The proposed procedure results in a posterior inference scheme that allows an automatic selection of the kernel parameters relevant to the problem at hand. In a series of synthetic experiments and two real data experiments (i.e., testing network heterogeneity from high-dimensional data and six-membered monocyclic ring conformation comparison), we illustrate the advantages of our approach. Supplementary materials for this article are available online.
AU  - Zhang,Q
AU  - Wild,V
AU  - Filippi,S
AU  - Flaxman,S
AU  - Sejdinovic,D
DO  - 10.1080/10618600.2022.2067547
EP  - 1176
PY  - 2022///
SN  - 1061-8600
SP  - 1164
TI  - Bayesian kernel two-sample testing
T2  - Journal of Computational and Graphical Statistics
UR  - http://dx.doi.org/10.1080/10618600.2022.2067547
UR  - http://arxiv.org/abs/2002.05550v2
UR  - https://www.tandfonline.com/doi/full/10.1080/10618600.2022.2067547
UR  - http://hdl.handle.net/10044/1/97109
VL  - 31
ER  -
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