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Positron annihilation is a well-established technique for producing spectra which can be analyzed for extracting physically meaningful parameters that characterize material defects and vacancies on an atomic scale. Mathematically, this is based on fitting a parameter-dependent model to the experimental data. Traditionally, this fit involves local nonlinear optimization routines that depend on a reasonable...
This article presents a method for decomposing a temporal sequence of photoelectron spectra into a parameter set reflecting the positions, amplitudes, and widths of the peaks. Since the peaks exhibit a slow evolution with time, we propose to take into account this temporal information by jointly decomposing the whole sequence. To this end, we have developed a Bayesian model where a Gaussian Markov...
This letter addresses the problem of decomposing a sequence of spectroscopic signals: data are a series of (energy or electromagnetic) spectra and we aim to estimate the peak parameters (centers, amplitudes, and widths). The key idea is to perform the decomposition of the whole sequence and to impose the parameters to evolve smoothly through the sequence. The problem is set within a Bayesian framework...
In this study, electroencephalography (EEG) inverse problem is formulated using Bayesian inference. The posterior probability distribution of current sources is sampled by Markov Chain Monte Carlo (MCMC) methods. Sampling algorithm is designed by combining Reversible Jump (RJ) which permits trans-dimensional iterations and Simulated Annealing (SA), a heuristic to escape from local optima. Two different...
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