Default prior for robust Bayesian model selection of sinusoids in Gaussian noise
We address the problem of detection and estimation of sinusoids embedded in white Gaussian noise. We follow a Bayesian approach and adopt robust default priors, expected posterior priors. In order to compute the associated Bayes factor required for model selection we resort to Monte Carlo Markov chain algorithms, and illustrate performance on an example.