A Bayesian formulation of exploratory data analysis and goodness‐of‐fit testing
International Statistical Review, 71 (2):
369--382 (августа 2003)DOI: 10.1111/j.1751-5823.2003.tb00203.x
Аннотация
Exploratory data analysis (EDA) and Bayesian inference (or, more generally, complex statistical modeling)—which are generally considered as unrelated statistical paradigms—can be particularly effective in combination. In this paper, we present a Bayesian framework for EDA based on posterior predictive checks. We explain how posterior predictive simulations can be used to create reference distributions for EDA graphs, and how this approach resolves some theoretical problems in Bayesian data analysis. We show how the generalization of Bayesian inference to include replicated data yrep and replicated parameters θrep follows a long tradition of generalizations in Bayesian theory.