  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 processes of manual parameter adjustment by analysts
or optimization using gradient methods. These analysis processes tend to
involve heuristic approaches and may lead to local solutions.Furthermore, it is
difficult to evaluate the reliability of the results obtained by conventional
analysis methods. Our method solves these problems by estimating model
parameters as probability distributions from SAS data using the framework of
the Bayesian inference. We evaluate the performance of our method through
numerical experiments using artificial data of representative measurement
target models.From the results of the numerical experiments, we show that our
method provides not only high accuracy and reliability of estimation, but also
perspectives on the transition point of estimability with respect to the
measurement time and the lower bound of the angular domain of the measured
data.
