MCMC for normalized random measure mixture models
This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Mont...
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| Format: | Journal article |
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Institute of Mathematical Statistics
2013
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| _version_ | 1797067442602639360 |
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| author | Favaro, S Teh, Y |
| author_facet | Favaro, S Teh, Y |
| author_sort | Favaro, S |
| collection | OXFORD |
| description | This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Monte Carlo methods of both marginal type and conditional type. The proposed marginal samplers are generalizations of Neal’s well-regarded Algorithm 8 for Dirichlet process mixture models, whereas the conditional sampler is a variation of those recently introduced in the literature. For both the marginal and conditional methods, we consider as a running example a mixture model with an underlying normalized generalized Gamma process prior, and describe comparative simulation results demonstrating the efficacies of the proposed methods. |
| first_indexed | 2024-03-06T21:56:20Z |
| format | Journal article |
| id | oxford-uuid:4d0afe47-1903-4da5-b3e3-8e8080fc988f |
| institution | University of Oxford |
| last_indexed | 2024-03-06T21:56:20Z |
| publishDate | 2013 |
| publisher | Institute of Mathematical Statistics |
| record_format | dspace |
| spelling | oxford-uuid:4d0afe47-1903-4da5-b3e3-8e8080fc988f2022-03-26T15:53:06ZMCMC for normalized random measure mixture modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4d0afe47-1903-4da5-b3e3-8e8080fc988fSymplectic Elements at OxfordInstitute of Mathematical Statistics2013Favaro, STeh, YThis paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in Bayesian nonparametric mixture models with normalized random measure priors. Making use of some recent posterior characterizations for the class of normalized random measures, we propose novel Markov chain Monte Carlo methods of both marginal type and conditional type. The proposed marginal samplers are generalizations of Neal’s well-regarded Algorithm 8 for Dirichlet process mixture models, whereas the conditional sampler is a variation of those recently introduced in the literature. For both the marginal and conditional methods, we consider as a running example a mixture model with an underlying normalized generalized Gamma process prior, and describe comparative simulation results demonstrating the efficacies of the proposed methods. |
| spellingShingle | Favaro, S Teh, Y MCMC for normalized random measure mixture models |
| title | MCMC for normalized random measure mixture models |
| title_full | MCMC for normalized random measure mixture models |
| title_fullStr | MCMC for normalized random measure mixture models |
| title_full_unstemmed | MCMC for normalized random measure mixture models |
| title_short | MCMC for normalized random measure mixture models |
| title_sort | mcmc for normalized random measure mixture models |
| work_keys_str_mv | AT favaros mcmcfornormalizedrandommeasuremixturemodels AT tehy mcmcfornormalizedrandommeasuremixturemodels |