Fast Bayesian Deconvolution Using Simple Reversible Jump Moves

We propose a Markov chain Monte Carlo-based deconvolution method designed to estimate the number of peaks in spectral data, along with optimal parameters each radial basis function. Assuming cases where is unknown, and sweep simulation on all candidate models computationally unrealistic, proposed efficiently searches over probable candidates via trans-dimensional moves assisted by annealing effects from replica exchange Carlo moves. Through using synthetic demonstrates its advantages conventional simulations, particularly model selection problems. Application set olivine reflectance data varying forsterite fayalite mixture ratios reproduced results obtained previous mineralogical research, indicating that our applicable real sets.