Markov Chain Sampling Methods for Dirichlet Process Mixture Models
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Abstract
Abstract This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis—Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors.
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The record
- Venue
- Journal of Computational and Graphical Statistics
- Topic
- Bayesian Methods and Mixture Models
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Keywords
- Gibbs samplingDirichlet distributionHierarchical Dirichlet processMarkov chain Monte CarloMetropolis–Hastings algorithmMarkov chainDirichlet processMathematicsPrior probabilityConjugate priorComputer scienceSampling (signal processing)Mathematical optimizationApplied mathematicsStatisticsBayesian probability
- Has abstract in OpenAlex
- yes