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Inference in DPM and PYM is typically performed using Markov Chain Monte Carlo (MCMC) methods, specifically the Gibbs sampler. These sampling methods are usually divided into two classes: marginal and conditional algorithms. Each method has its own merits and limitations. The aim of this paper is to propose a simple and effective strategy that combines the main advantages of each class. Extensive experiments on simulated and real data highlight that the proposed sampler is relevant and performs much better than its competitors.<\/jats:p>","DOI":"10.1007\/s00180-025-01637-y","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T02:03:47Z","timestamp":1748657027000},"page":"4675-4716","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A simple and efficient method for sampling mixture models based on Dirichlet and Pitman-Yor processes"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2749-2589","authenticated-orcid":false,"given":"Mame Diarra","family":"Fall","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c9ric","family":"Barat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"1637_CR1","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.csda.2014.12.003","volume":"93","author":"J Arbel","year":"2016","unstructured":"Arbel J, Lijoi A, Nipoti B (2016) Full Bayesian inference with hazard mixture models. 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