Gibbs Sampling for Signal Reconstruction

This paper describes the use of stochastic simulation techniques to reconstruct biomedical signals not directly measurable. In particular, a deconvolution problem with an uncertain clearance parameter is considered. The problem is addressed using a Monte Carlo Markov Chain method, called the Gibbs Sampling, in which the joint posterior probability distribution of the stochastic parameters is estimated through sampling from conditional distributions. This method provides a fully bayesian solution to signal smoothing and deconvolution.