2021/03/12 by Ian Langmore, M. Dikovsky, Langmore, Ian +7 · 1 citation
Decision Sciences · Engineering · Physics and Astronomy · #90-08 #Computation (stat.CO) #FOS: Computer and information sciences #G.3 #Nuclear Physics and Applications #Nuclear reactor physics and engineering #Scientific Measurement and Uncertainty Evaluation
paper · pdf · doi:10.48550/arxiv.2103.07515
openalex publication_date 2021/03/12 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Modern auto-differentiating software should allow more widespread usage in Bayesian inverse problems. This paper analyzes two major difficulties encountered using HMC for inverse problems: poor conditioning and multi-modality. Novel results on preconditioning and replica exchange Monte Carlo parameter selection are presented in the context of spectroscopy. Recommendations are given for the number of integration steps as well as step size, preconditioner type and fitting, annealing form and schedule. These recommendations are analyzed rigorously in the Gaussian case, and shown to generalize in a fusion plasma reconstruction.