The use of bootstrap in computer-intensive Bayesian methods

The use of computer-intensive methods in signal processing becomes more frequent as the power of computers continues to increase. Two classes of such methods are Bayesian Monte Carlo sampling and the bootstrap. In general, these types of methods are used in different settings. The Bayesian methods are usually applied in situations where parametric assumptions are made about the densities that generate the observed data, and the bootstrap, in cases where such assumptions are absent. We explore the possibility of combining these methods. The role of the bootstrap is to provide samples for constructing density functions needed for drawing samples which would allow for more accurate integration or optimization carried out by the Bayesian methods.