  Recent advances in adaptive Markov chain Monte Carlo (AMCMC) include the need
for regional adaptation in situations when the optimal transition kernel is
different across different regions of the sample space. Motivated by these
findings, we propose a mixture-based approach to determine the partition needed
for regional AMCMC. The mixture model is fitted using an online EM algorithm
(see Andrieu and Moulines, 2006) which allows us to bypass simultaneously the
heavy computational load and to implement the regional adaptive algorithm with
online recursion (RAPTOR). The method is tried on simulated as well as real
data examples.
