  Motivated by the physics of strings and branes, we introduce a general suite
of Markov chain Monte Carlo (MCMC) "suburban samplers" (i.e., spread out
Metropolis). The suburban algorithm involves an ensemble of statistical agents
connected together by a random network. Performance of the collective in
reaching a fast and accurate inference depends primarily on the average number
of nearest neighbor connections. Increasing the average number of neighbors
above zero initially leads to an increase in performance, though there is a
critical connectivity with effective dimension d_eff ~ 1, above which
"groupthink" takes over, and the performance of the sampler declines.
