  This paper presents an algorithm for sampling random variables that allows to
separation of the sampling process into subproblems by dividing the sample
space into overlapping parts. The subproblems can be solved independently of
each other and are thus well suited for parallelization. Furthermore, on each
of these subproblems it is possible to use distinct and independent sampling
methods. In other words, specific samplers can be designed for specific parts
of the sample space. The algorithms are demonstrated on a particle marginal
Metropolis-Hastings sampler applied to calibration of a volatility model and
two toy examples. Significant speedup and decrease of total variation is
observed in experiments.
