Two-sample Bayesian nonparametric hypothesis testing
CARON, Francois
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
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Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
CARON, Francois
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
< Leer menos
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
Idioma
en
Rapport
Este ítem está publicado en
2009
Resumen en inglés
In this article we describe Bayesian nonparametric procedures for two-sample hypothesis testing. Namely, given two sets of samples y^{(1)} iid F^{(1)} and y^{(2)} iid F^{(2)}, with F^{(1)}, F^{(2)} unknown, we wish to ...Leer más >
In this article we describe Bayesian nonparametric procedures for two-sample hypothesis testing. Namely, given two sets of samples y^{(1)} iid F^{(1)} and y^{(2)} iid F^{(2)}, with F^{(1)}, F^{(2)} unknown, we wish to evaluate the evidence for the null hypothesis H_{0}:F^{(1)} = F^{(2)} versus the alternative. Our method is based upon a nonparametric Polya tree prior centered either subjectively or using an empirical procedure. We show that the Polya tree prior leads to an analytic expression for the marginal likelihood under the two hypotheses and hence an explicit measure of the probability of the null Pr(H_{0}|y^{(1)},y^{(2)}).< Leer menos
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