Bayesian Logical Data Analysis for the Physical Sciences
Bayesian Logical Data Analysis for the Physical Sciences
Estimating parameters of sinusoids from noisy data using Bayesian inference with simulated annealing
WSEAS Transactions on Signal Processing
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
IEEE Transactions on Signal Processing
A model selection rule for sinusoids in white Gaussian noise
IEEE Transactions on Signal Processing
Bayesian deconvolution of noisy filtered point processes
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
In this paper, we consider a problem of detecting and estimating of sinusoids corrupted by random noise within a Bayesian framework. Unfortunately, all Bayesian inference drawn from posterior probability distributions of parameters requires evaluation of some complicated high-dimensional integrals. Therefore, an attempt for performing the Bayesian computation is made to improve an efficient stochastic algorithm based on reversible jump Markov chain Monte Carlo (RJMCMC) methods. This algorithm, coded in Mathematica programming language is evaluated in simulation studies on synthetic data sets. All the simulations results support the effectiveness of the method.