Bayesian Parameter Estimation of Sinusoids with Simulated Annealing

Abstract

In this paper, we consider Bayesian analysis proposed by Bretthorst for estimating parameters from noisy data and combined it with a simulated annealing algorithm to obtain a global maximum of the posterior probability density of 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 and used it for estimating parameters of sinusoids corrupted by random noise. The simulations results support the effectiveness of the method.