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A Split-Merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Jain, Sonia Neal, Radford |
| Abstract | . We propose a split-merge Markov chain algorithm to address the problem of inefficient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian mixture models, such as Gibbs sampling, can become trapped in isolated modes corresponding to an inappropriate clustering of data points. This article describes a Metropolis-Hastings procedure that can escape such local modes by splitting or merging mixture components. Our Metropolis-Hastings algorithm employs a new technique in which an appropriate proposal for splitting or merging components is obtained by using a restricted Gibbs sampling scan. We demonstrate empirically that our method outperforms the Gibbs sampler in situations where two or more components are similar in structure. Key words: Dirichlet process mixture model, Markov chain Monte Carlo, Metropolis-Hastings algorithm, Gibbs sampler, split-merge updates 1 Introduction Mixture models are often applied to density estim... |
| File Format | |
| Volume Number | 13 |
| Journal | Journal of Computational and Graphical Statistics |
| Language | English |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Dirichlet Process Mixture Model Metropolis-hastings Algorithm Gibbs Sampler Local Mode New Technique Data Point Split-merge Update Appropriate Proposal Conjugate Dirichlet Process Mixture Model Metropolis-hastings Procedure Inappropriate Clustering Bayesian Mixture Model Key Word Mixture Component Isolated Mode Split-merge Markov Chain Algorithm Gibbs Sampling Density Estim Introduction Mixture Model Markov Chain Monte Carlo |
| Content Type | Text |
| Resource Type | Article |
