Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity

TL;DR

A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed and two experimental results show feasibility of the proposed method.

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.

Figures (13)

• Figure 1: Inverse Estimation of SMI
• Figure 2: Stochastic model illustration
• Figure 3: SAS experiment set-up
• Figure 4: Plateau Pattern inference
• Figure 5: Two-peak pettern inference