{"id":"W2080972498","doi":"10.1080/10618600.2000.10474879","title":"Markov Chain Sampling Methods for Dirichlet Process Mixture Models","year":2000,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2212,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gibbs sampling; Dirichlet distribution; Hierarchical Dirichlet process; Markov chain Monte Carlo; Metropolis–Hastings algorithm; Markov chain; Dirichlet process; Mathematics; Prior probability; Conjugate prior; Computer science; Sampling (signal processing); Mathematical optimization; Applied mathematics; Statistics; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02605654607133213,"score_gpt":0.3550975326748263,"score_spread":0.3290409866034942,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}