# Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity

arXiv: https://arxiv.org/abs/2411.05805 · HTML: https://mlsanity.com/paper/2411.05805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Machine Learning-Assisted Analysis of Small Angle X-ray Scattering](https://mlsanity.com/paper/2111.08645/) (2021)

## Abstract (arXiv)

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.