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- W2062373184 abstract "The inferential problem of associating data to mixture components is difficult when components are nearby or overlapping. We introduce a new split-merge Markov chain Monte Carlo technique that efficiently classifies observations by splitting and merging mixture components of a nonconjugate Dirichlet process mixture model. Our method, which is a Metropolis-Hastings procedure with split-merge proposals, samples clusters of observations simultaneously rather than incrementally assigning observations to mixture components. Split-merge moves are produced by exploiting properties of a restricted Gibbs sampling scan. A simulation study compares the new split-merge technique to a nonconjugate version of Gibbs sampling and an incremental Metropolis-Hastings technique. The results demonstrate the improved performance of the new sampler." @default.
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- W2062373184 date "2007-09-01" @default.
- W2062373184 modified "2023-10-17" @default.
- W2062373184 title "Splitting and merging components of a nonconjugate Dirichlet process mixture model" @default.
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- W2062373184 doi "https://doi.org/10.1214/07-ba219" @default.
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