  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 demonstrate how the algorithm can overcome those
pathologies in practical applications.
