Bayesian Kernelised Test of (In)dependence with Mixed-type Variables

Alessio Benavoli; Cassio de Campos

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Bayesian Kernelised Test of (In)dependence with Mixed-type Variables

Alessio Benavoli, Cassio de Campos

Machine Learning · Submitted 9 May 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,462 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.06 · human participants 0.05 · controlled comparison 0.45 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.97 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound).	background 1.00	0.05	0.03	asserted 1.00
s1	We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model.	contribution 0.81	0.93	0.14	asserted 1.00
s2	The new measure of (in)dependence allows us to answer some fundamental questions: Based on data, are (mixed-type) variables independent?	contribution 0.77	0.61	0.18	asserted 0.94
s3	How likely is dependence/independence to hold?	contribution 0.61	0.17	0.05	unclear 0.89
s4	How high is the probability that two mixed-type variables are more than just weakly dependent?	contribution 0.67	0.35	0.07	unclear 0.81
s5	We theoretically show the properties of the approach, as well as algorithms for fast computation with it.	contribution 0.77	0.95	0.06	asserted 1.00
s6	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.	contribution 0.50	0.96	0.41	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.45.

What was examined, and what was not

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arXiv listing abstract v1

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full text
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P abstract

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S sections
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original 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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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For instance, Figure [ref] illustrates the application of BKR to the Houses dataset [ref] that combines visual (4 images tiled together), numeric (number of bedrooms and bathrooms, area) and textual (location) attributes to be used for housing price estimation.

House price estimation from visual and textual features · Introduction · 0.98

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