ArticleTest (Madrid, Spain)2026
Analytic posterior distribution and bayes factor for pearson partial correlations.
Article in Test (Madrid, Spain), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Multiscale parcellation of dynamic causal models of the brain.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
4 authors.
Funding
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Abstract
This article outlines a novel Bayesian approach to the testing and estimation of Pearson partial correlations. By generalizing a Bayesian inference procedure for Pearson's correlation coefficient, we obtain analytic expressions for the Bayes factor and for the (marginal) posterior distribution of a partial correlation coefficient. Full Bayesian inference can be achieved using only the sample size, the number of controlling variables and the relevant summary statistics, that is, the sample partial correlation. The present approach is illustrated with two empirical examples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.