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Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel "vertical" chains are led by random-walk proposals, whereas the "horizontal" MCMC uses a independentdoi:10.1109/ssp.2014.6884651 dblp:conf/ssp/MartinoELAC14 fatcat:gfliik5lhreuzf4rgi5okjqd4i