example_tau. eps - arXiv. org Hierarchical modeling provides a framework for modeling the complex interactions typical of problems in applied statistics By capturing these relationships, however, hierarchical models also introduce distinctive pathologies that quickly limit the efficiency of most common methods of in-ference In this paper we explore the use of Hamiltonian Monte Carlo for hierarchical models and
c:\aop\27-2\AOP095 - Project Euclid Universidad de Valladolid, University of Connecticut and Universidad de Valladolid If is integrable, F is its cdf and Fn is the empirical cdf based on an i i d sample from F, then the Wasserstein distance between Fn and F, which coincides with the L1 norm −∞ Fn t− F t dt of the centered empirical process, tends to zero a s The object of this article is to obtain rates of convergence