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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 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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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 and predictive copula
4 open, 2022 In this paper, we address the concept of conditional independence between two random variables X and Y given the entity Θ. We identify the impact of conditional independence on the analytic form of the predictive 2-copula between X and Y.
González-López V.A. +1 more
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Conditional Independence by Typing [PDF]
ACM Transactions on Programming Languages and Systems, 2021 A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to re-write their models to improve efficiency of inference or meet restrictions imposed by the PPL.
Gorinova, Maria I. +3 more
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Conditional Sure Independence Screening [PDF]
Journal of the American Statistical Association, 2016 Independence screening is a powerful method for variable selection for `Big Data' when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or variations of it. In many applications, researchers often have some prior knowledge that a certain set of variables is related to the response.
Barut, Emre +2 more
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Describing Conditional Independence Statements Using Undirected Graphs
Axioms, 2023 This paper investigates the capability of undirected graphs (UGs) to represent a set of Conditional Independence (CI) statements derived from a given probability distribution of a random vector. While it is established that certain axioms can govern this
Dhafer Malouche
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Bayesian test of independence and conditional independence of two ordinal variables [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 For analysis of contingency tables with large sample size, classical approaches using approximate methods have high power. However, when the sample size is small or some cells have frequencies less than 5, classical approaches are so conservative.
Zahra Saberi, Mojtab Ganjali
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