Recovering sinusoids from noisy data using bayesian inference with simulated annealing - (:unkn) unknown (My University, 2011)
In this paper, we studied Bayesian analysis proposed by Bretthorst[6] for a general signal model equation and combined it with a simulated annealing (SA) algorithm to obtain a global maximum of a posterior probability density function (PDF) for frequencies. Thus, this analysis offers different approach to finding parameter values through a directed, but random, search of the parameter space. For this purpose, we developed a Mathematica code of this Bayesian approach together with SA and used it for recovering sinusoids from noisy data. Simulations results support its effectiveness. Copyright © Association for Scientific Research.
Keywords: Bayesian Statistical Inference Simulated Annealing; Cramér-Rao lower bound; Parameter estimations; Power Spectral Density