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Title: Bayesian nonparametric hypothesis testing procedures Authors:  Luis Gutierrez - Pontificia Universidad Catolica de Chile (Chile) [presenting]
Abstract: Scientific knowledge is firmly based on the use of statistical hypothesis testing procedures. A scientific hypothesis can be established by performing one or many statistical tests based on the evidence provided by the data. Given the importance of hypothesis testing in science, these procedures are an essential part of statistics. The literature on hypothesis testing is vast and covers a wide range of practical problems. However, most of the methods are based on restrictive parametric assumptions. We will discuss Bayesian nonparametric approaches to construct hypothesis tests in different contexts. Our proposal resorts to the literature of model selection to define Bayesian tests for multiple samples, paired-samples, and longitudinal data analysis. Applications with real-life datasets and illustrations with simulated data will be discussed.