Extending Hilbert–Schmidt Independence Criterion for Testing Conditional Independence [PDF]
Entropy, 2023 The Conditional Independence (CI) test is a fundamental problem in statistics. Many nonparametric CI tests have been developed, but a common challenge exists: the current methods perform poorly with a high-dimensional conditioning set.
Bingyuan Zhang, Joe Suzuki
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A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test [PDF]
Entropy, 2022 In this study, we focus on mixed data which are either observations of univariate random variables which can be quantitative or qualitative, or observations of multivariate random variables such that each variable can include both quantitative and ...
Lei Zan +4 more
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Do rats learn conditional independence? [PDF]
Royal Society Open Science, 2017 If acquired associations are to accurately represent real relevance relations, there is motivation for the hypothesis that learning will, in some circumstances, be more appropriately modelled, not as direct dependence, but as conditional independence. In
Robert Ian Bowers, William Timberlake
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Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers [PDF]
BMC Bioinformatics, 2009 The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants.
Biganzoli Elia +2 more
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Conditional independence as a statistical assessment of evidence integration processes. [PDF]
PLoS ONEIntuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are ...
Emilio Salinas, Terrence R Stanford
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Transitional Conditional Independence [PDF]
, 2021 We develope the framework of transitional conditional independence. For this we introduce transition probability spaces and transitional random variables. These constructions will generalize, strengthen and unify previous notions of (conditional) random variables and non-stochastic variables, (extended) stochastic conditional independence and some form
Patrick Forré
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Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates. [PDF]
PLoS Computational Biology, 2015 Functional connectivity concerns the correlated activity between neuronal populations in spatially segregated regions of the brain, which may be studied using functional magnetic resonance imaging (fMRI).
Max Hinne +3 more
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Conditional Independence for Statistical Operations [PDF]
The Annals of Statistics, 1980 A general calculus of conditional independence is developed, suitable for application to a wide range of statistical concepts such as sufficiency, parameter-identification, adequacy and ancillarity. A vehicle for this theory is the statistical operation, a structure-preserving map between statistical spaces.
A. P. Dawid
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Empirical Bayes conditional independence graphs for regulatory network recovery. [PDF]
Bioinformatics, 2012 Mahdi R +7 more
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Detecting departures from the conditional independence assumption in diagnostic latent class models: a simulation study. [PDF]
BMC Med Res MethodolOkkaoglu Y, Welton NJ, Jones HE.
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