Hamiltonian Monte Carlo for Hierarchical Models
Methodology
2013-12-04 v1
Abstract
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.
Cite
@article{arxiv.1312.0906,
title = {Hamiltonian Monte Carlo for Hierarchical Models},
author = {M. J. Betancourt and Mark Girolami},
journal= {arXiv preprint arXiv:1312.0906},
year = {2013}
}
Comments
11 pages, 12 figures