Add to ORKGTests 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 sh...
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM...
The problem of dependence in the outcome variables has become an increasingly important issue of con...
This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a d...
Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous ...
\u3cp\u3eTests for dependence of continuous, discrete and mixed continuous-discrete variables are ub...
Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are...
Conditional independence tests have received special attention lately in machine learning and comput...
We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correla...
The differential diagnosis of a disease is often based on the information obtained from multiple dia...
The definition of vectors of dependent random probability measures is a topic of interest in Bayesi...
This paper provides a general methodology for testing for dependence in time series data, with parti...
In hypothesis testing, the conclusions from Bayesian and Frequentist approaches can differ markedly,...
SUMMARY. Many analyses of results from multiple diagnostic tests assume the tests are statistically ...
Conditional independence tests (CI tests) have received special at-tention lately in Machine Learnin...
This paper provides a general methodology for testing for dependence in time series data, with parti...
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM...
The problem of dependence in the outcome variables has become an increasingly important issue of con...
This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a d...
Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous ...
\u3cp\u3eTests for dependence of continuous, discrete and mixed continuous-discrete variables are ub...
Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are...
Conditional independence tests have received special attention lately in machine learning and comput...
We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correla...
The differential diagnosis of a disease is often based on the information obtained from multiple dia...
The definition of vectors of dependent random probability measures is a topic of interest in Bayesi...
This paper provides a general methodology for testing for dependence in time series data, with parti...
In hypothesis testing, the conclusions from Bayesian and Frequentist approaches can differ markedly,...
SUMMARY. Many analyses of results from multiple diagnostic tests assume the tests are statistically ...
Conditional independence tests (CI tests) have received special at-tention lately in Machine Learnin...
This paper provides a general methodology for testing for dependence in time series data, with parti...
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM...
The problem of dependence in the outcome variables has become an increasingly important issue of con...
This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a d...