Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity
Machine Learning
2024-11-12 v1 Computational Engineering, Finance, and Science
Signal Processing
Abstract
A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed in this paper. The data is popular to obtain information about unobservable features of an object, such as a material sample and the components of it. The proposed method assumes particles represent the wave, and their behaviors are stochastically modeled. The inference is accurate even if the data is noisy because of a smooth prior setting. Moreover, in this paper, two experimental results show feasibility of the proposed method.
Cite
@article{arxiv.2411.05805,
title = {Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity},
author = {Akinori Asahara and Yoshihiro Osakabe and Yamamoto Mitsuya and Hidekazu Morita},
journal= {arXiv preprint arXiv:2411.05805},
year = {2024}
}