{"id":"W2278730005","doi":"10.48550/arxiv.1508.02663","title":"Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Gibbs sampling; Merge (version control); Inference; Bayesian inference; Bayesian probability; Computer science; Econometrics; Statistics; Mathematics; Statistical physics; Artificial intelligence; Physics; Information retrieval","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009189931,0.0004119067,0.0005422687,0.0002243976,0.0001100472,0.0001610308,0.001928126,0.0004779109,0.000007587777],"category_scores_gemma":[0.0001191508,0.0004585777,0.000232209,0.000636727,0.00007267887,0.000662986,0.001462328,0.0006694358,0.000009621345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002395297,"about_ca_system_score_gemma":0.0004684207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000163333,"about_ca_topic_score_gemma":0.0001346817,"domain_scores_codex":[0.997251,0.0002235992,0.0003344912,0.001427835,0.000118221,0.0006448613],"domain_scores_gemma":[0.997478,0.0002903094,0.0002278192,0.001407247,0.0002658858,0.000330756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003513278,0.00007954077,0.0003182601,0.0000828424,0.00002795495,0.00005106183,0.0006218585,0.2615716,0.00003040883,0.7323915,0.0001008016,0.004689063],"study_design_scores_gemma":[0.0003405759,0.00002526718,0.00003867018,0.00006721797,0.00001884288,8.457991e-7,0.00001331496,0.5317714,0.00006036546,0.4672394,0.0001345986,0.0002895907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03127978,0.0002313558,0.9659575,0.0002038524,0.0004953732,0.0005898174,0.00003116727,0.0001752523,0.001035961],"genre_scores_gemma":[0.7662602,0.00007771159,0.2329541,0.0001215285,0.00007160792,0.000006120426,0.00001109009,0.00002241584,0.0004752066],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7349805,"threshold_uncertainty_score":0.9997866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1792049418121765,"score_gpt":0.259742621568692,"score_spread":0.08053767975651546,"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."}}