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Analytic posteriors for Pearson's correlation coefficient
Author(s) -
Ly Alexander,
Marsman Maarten,
Wagenmakers EricJan
Publication year - 2018
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12111
Subject(s) - pearson product moment correlation coefficient , prior probability , correlation coefficient , mathematics , measure (data warehouse) , statistics , bayesian probability , bayes' theorem , correlation , distance correlation , class (philosophy) , posterior probability , computer science , artificial intelligence , data mining , random variable , geometry
Pearson's correlation is one of the most common measures of linear dependence. Recently, Bernardo (11th International Workshop on Objective Bayes Methodology, 2015) introduced a flexible class of priors to study this measure in a Bayesian setting. For this large class of priors, we show that the (marginal) posterior for Pearson's correlation coefficient and all of the posterior moments are analytic. Our results are available in the open‐source software package JASP.