TGMCMC is a hybrid posterior inference algorithm for normalized random measure mixture models that combines MCMC sampler and tree-based discrete approximate algorithm. It builds binary trees representing the cluster structure of datasets via incremental BHC, and proposes new clustering results based on those trees and accept the proposals with Metropolis-Hastings algorithm. TGMCMC mixes much faster than conventional MCMC algorithms, yet is guaranteed to converge unlike incremental BHC.
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