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An improved and robust Bayesian method is proposed to estimate the number-weighted Particle Size-Distributions (PSD) from data obtained by Multiangle Dynamic Light Scattering (MDLS). Compared to former approach presented by Clementi, the originality of our method lies in the fact that it is directly applied to raw MDLS data without any preprocessing. Indeed, the PSD Bayesian estimation proposed by...
The inverse problem of estimating the Particle Size Distribution (PSD) from Multiangle Dynamic Light Scattering measurements (MDLS) is considered using a Bayesian inference approach. We propose to model the multimodal PSD as a normal mixture with an unknown number of components (modes or peaks). In order to achieve the estimation of these variable dimension parameters, a Bayesian inference approach...
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