Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods"
Simon Barthelme, Magali Beffy, Nicolas Chopin, Arnaud Doucet, Pierre, Jacob, Adam M. Johansen, Jean-Michel Marin, and Christian P. Robert
TL;DR
This paper compiles discussions on advanced Riemann manifold Monte Carlo methods, highlighting their theoretical foundations and practical implications for Bayesian computation.
Contribution
It provides a comprehensive collection of expert opinions and insights on Riemann manifold Langevin and Hamiltonian Monte Carlo methods, emphasizing their significance and future directions.
Findings
Enhanced understanding of Riemann manifold Monte Carlo techniques
Identification of key challenges and opportunities in the field
Suggestions for improving algorithm efficiency and applicability
Abstract
This is a collection of discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead, to appear in the Journal of the Royal Statistical Society, Series B.
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Stochastic processes and statistical mechanics · Bayesian Methods and Mixture Models