Physical mixture modeling with unknown number of components
Measured physical spectra often comprise an unknown number of components of known parametric family. A reversible jump Markov chain Monte Carlo (RJMCMC) technique is applied to the problem of estimating the number of components evident in the data jointly with the parameters of the components. The physical model consists of a mixture of components, an additive background, and a convolution with a blurring apparatus transfer function. The results were compared with the deconvolution of a form-free distribution. By calculating marginal posterior probability density distributions from the RJMCMC sample for the most probable number of components we estimated the parameters and their uncertainties. The method was applied to a benchmark test of Rutherford backscattering spectroscopy on a system consisting of a thin copper film where we know that copper consists of two isotopes.
Bibliographic Reference: An article published in: Bayesian inference and maximum entropy methods in science and engineering, 21st International Workshop, edited by R.L.Fry, American Institute of Physics, 2002, pp.143-154.
Record Number: 200214942 / Last updated on: 2002-07-09
Original language: en
Available languages: en