  Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays,
are employed in various scientific fields, including materials science,
biochemistry, and polymer physics. During the analysis of SAS data, model
parameters that contain information about the sample are estimated by fitting
the observational data to a model of sample. Previous research has demonstrated
the effectiveness of Bayesian inference in analyzing SAS data using a sphere
model. However, compared with the sphere model, the core-shell model, which
represents functional nanoparticles, offers higher application potential and
greater analytical value. Therefore, in this study, we propose an analytical
method for the more complex and practical core-shell model based on Bayesian
inference. Through numerical experiments, we evaluated the performance of this
method under different conditions, including measurement times, number of data
points, and differences in scattering length density. As a result, we clarify
the conditions under which accurate estimations are possible.
