Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo

Jiang, Zhang; Wang, Jin; Tirrell, Matthew V.; de Pablo, Juan J.; Chen, Wei

doi:10.1107/S1600577522003034

Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo

Journal Article · Fri Apr 22 00:00:00 EDT 2022 · Journal of Synchrotron Radiation (Online)

DOI:https://doi.org/10.1107/S1600577522003034· OSTI ID:1864365

Jiang, Zhang; Wang, Jin; Tirrell, Matthew V.; de Pablo, Juan J.;

Bayesian-inference-based approaches, in particular the random-walk Markov Chain Monte Carlo (MCMC) method, have received much attention recently for X-ray scattering analysis. Hamiltonian MCMC, a state-of-the-art development in the field of MCMC, has become popular in recent years. It utilizes Hamiltonian dynamics for indirect but much more efficient drawings of the model parameters. We described the principle of the Hamiltonian MCMC for inversion problems in X-ray scattering analysis by estimating high-dimensional models for several motivating scenarios in small-angle X-ray scattering, reflectivity, and X-ray fluorescence holography. Hamiltonian MCMC with appropriate preconditioning can deliver superior performance over the random-walk MCMC, and thus can be used as an efficient tool for the statistical analysis of the parameter distributions, as well as model predictions and confidence analysis.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1864365

Alternate ID(s):: OSTI ID: 1894232

Journal Information:: Journal of Synchrotron Radiation (Online), Journal Name: Journal of Synchrotron Radiation (Online) Journal Issue: 3 Vol. 29; ISSN 1600-5775; ISSN JSYRES

Publisher:: International Union of Crystallography (IUCr)Copyright Statement

Country of Publication:: Denmark

Language:: English

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