Nested Adaptation of MCMC Algorithms

Authors

Dao Nguyen, University of Mississippi
Perry de Valpine, Department of Environmental Science, Policy, and Management
Yves Atchade, University of Michigan, Ann Arbor
Daniel Turek, Williams College
Nicholas Michaud, Department of Environmental Science, Policy, and Management
Christopher Paciorek, University of California, Berkeley

Document Type

Article

Publication Date

1-1-2020

Abstract

© 2020 International Society for Bayesian Analysis Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open question. This paper introduces a novel MCMC algorithm, namely, Nested Adaptation MCMC. For sampling variables or blocks of variables, we use two levels of adaptation where the inner adaptation optimizes the MCMC performance within each sampler, while the outer adaptation explores the space of valid kernels to find the optimal samplers. We provide a theoretical foundation for our approach. To show the generality and usefulness of the approach, we describe a framework using only standard MCMC samplers as candidate samplers and some adaptation schemes for both inner and outer iterations. In several benchmark problems, we show that our proposed approach substantially outperforms other approaches, including an automatic blocking algorithm, in terms of MCMC efficiency and computational time.

Relational Format

journal article

Recommended Citation

Nguyen, D., de Valpine, P., Atchade, Y., Turek, D., Michaud, N., & Paciorek, C. (2020). Nested Adaptation of MCMC Algorithms. Bayesian Analysis, 15(4), 1323–1343. https://doi.org/10.1214/19-BA1190

DOI

10.1214/19-BA1190

Accessibility Status

Searchable text