5 papers
Resolving the Effects of the Mixing Process on Hierarchical Structures in Polymer Nanocomposites by Bayesian Ultra-Small-Angle X-ray Scattering Analysis
Yui Hayashi, Kazuki Mita, Shigeo Kuwamoto +1
Bayesian inference was combined with Unified Fit to resolve the effects of the mixing history on polymer nanocomposites whose ultra-small-angle X-ray scattering profiles are nearly…
GPU-Accelerated Sequential Monte Carlo for Bayesian Spectral Analysis
Tomohiro Nabika, Yui Hayashi, Masato Okada
Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, it becomes…
Bayesian Inference for Small-Angle Scattering Data II: Core-Shell Samples
Keigo Oyama, Yui Hayashi, Shigeo Kuwamoto +4
Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physic…
Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian Methods
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical mo…
Bayesian Inference for Small-Angle Scattering Data
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve p…