Splitting and merging components of a nonconjugate Dirichlet process mixture model. (English) Zbl 1331.62145

Jain, Sonia; Neal, Radford M.

Bayesian Anal. 2, No. 3, 445-472 (2007).

Summary: 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.

MSC:

62F15	Bayesian inference
62M10	Time series, auto-correlation, regression, etc. in statistics (GARCH)
65C05	Monte Carlo methods
65C40	Numerical analysis or methods applied to Markov chains
62D05	Sampling theory, sample surveys

Keywords:

Bayesian model; Markov chain Monte Carlo; split-merge moves; nonconjugate prior