A reversible jump MCMC algorithm for Particle Size inversion in Multiangle Dynamic Light Scattering

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 is used and solved by the Reversible Jump Markov ChainMonte Carlo sampler (RJMCMC). The efficiency and robustness of the method proposed are demonstrated using simulated and experimental data. Estimated PSDs are close to the original distributions for synthetic data. Moreover an improvement of the resolution is noticed compared to the Clementi method [1].