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• W4229013544 startingPage "721" @default.
• W4229013544 abstract "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." @default.
• W4229013544 created "2022-05-08" @default.
• W4229013544 creator A5006465118 @default.
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• W4229013544 date "2022-04-22" @default.
• W4229013544 modified "2023-09-26" @default.
• W4229013544 title "Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo" @default.
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• W4229013544 doi "https://doi.org/10.1107/s1600577522003034" @default.
• W4229013544 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35511005" @default.
• W4229013544 hasPublicationYear "2022" @default.
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