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Bayesian Independence Test with Mixed-type Variables | IEEE Conference Publication | IEEE Xplore

Bayesian Independence Test with Mixed-type Variables


Abstract:

A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependenc...Show More

Abstract:

A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model. The new measure of (in)dependence allows us to answer some fundamental questions: Based on data, are (mixed-type) variables independent? How likely is dependence/independence to hold? How high is the probability that two mixed-type variables are more than just weakly dependent? We theoretically show the properties of the approach, as well as algorithms for fast computation with it. We empirically demonstrate the effectiveness of the proposed method by analysing its performance and by comparing it with other frequentist and Bayesian approaches on a range of datasets and tasks with mixed-type variables.
Date of Conference: 06-09 October 2021
Date Added to IEEE Xplore: 20 October 2021
ISBN Information:
Conference Location: Porto, Portugal

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