Benavoli, Alessio De Campos, Cassio P. 2016-09-06 Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the Dirichlet Process, which allows us to use the same prior model for all of them. Therefore, the tests are “consistent” among each other, in the sense that the probabilities that variables are dependent computed with these tests are commensurable across the different types of variables being tested. By means of simulations with artificial data, we show the effectiveness of the new tests. application/pdf https://localhost:5000/ark:/12658/srd1319047 https://susi.usi.ch/global/documents/319047 https://susi.usi.ch/documents/319047/files/Benavoli_E_2016.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.3390/e18090326 info:eu-repo/semantics/altIdentifier/ark/12658/srd1319047 info:eu-repo/semantics/openAccess CC BY Entropy. - 2016, vol. 18, no. 9, p. 326 Dependence Bayesian independence test Dirichlet process info:eu-repo/classification/udc/004 Bayesian dependence tests for continuous, binary and mixed continuous-binary variables http://purl.org/coar/resource_type/c_6501