BibTeX
@ARTICLE{
         Jiang2022Pef,
       author = "Jiang, Zhang and Wang, Jin and Tirrell, Matthew V. and de Pablo, Juan J. and Chen,
         Wei",
       journal = "Journal of Synchrotron Radiation",
       pages = "721--731",
       title = "Parameter estimation for {X}-ray scattering analysis with {H}amiltonian {M}arkov
         {C}hain {M}onte {C}arlo",
       year = "2022",
       volume = "29",
       number = "3",
       doi = "10.1107/S1600577522003034",
       url = "https://doi.org/10.1107/S1600577522003034",
       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.",
       keywords = "small-angle X-ray scattering, X-ray reflectivity, Markov chain Monte Carlo"
,
       ad_area = "Physics",
       ad_tools = "ADiMat"}