{"id":"W3001762958","doi":"10.48550/arxiv.2001.09367","title":"Particle-Gibbs Sampling For Bayesian Feature Allocation Models","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gibbs sampling; Markov chain Monte Carlo; Feature (linguistics); Computer science; Bayesian inference; Inference; Particle filter; Bayesian probability; Markov chain; Algorithm; Artificial intelligence; Data mining; Machine learning; Mathematical optimization; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004271009,0.001271587,0.001652215,0.001616224,0.0009367415,0.001883302,0.002884929,0.002159309,0.006238273],"category_scores_gemma":[0.02059194,0.001193194,0.001442994,0.002116349,0.001987558,0.002951907,0.001687881,0.003369697,0.001618157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002069615,"about_ca_system_score_gemma":0.002009355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01119703,"about_ca_topic_score_gemma":0.01074688,"domain_scores_codex":[0.9975963,0.00129684,0.00008325002,0.0004860915,0.0004127199,0.0001248829],"domain_scores_gemma":[0.9916301,0.006743595,0.0003436358,0.0006261833,0.0005158552,0.0001405838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001241736,0.00007474204,0.001509385,0.0001724676,0.00009902335,0.0001000208,0.0001238846,0.6820377,0.0006910727,0.2403356,0.005102368,0.0696296],"study_design_scores_gemma":[0.00001610703,0.000007618798,0.0001420082,0.00001120956,0.000006468526,0.00001692755,0.000005417525,0.8994327,0.0001685278,0.09883179,0.001350794,0.00001030563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001657072,0.000179558,0.9971098,0.0001284217,0.00002298522,0.00004035586,0.0001198761,0.0002470654,0.0004947993],"genre_scores_gemma":[0.1863077,0.0009526915,0.8041523,0.0003439798,0.0002446042,0.0009744138,0.001707271,0.0004192181,0.004897733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01119703,"threshold_uncertainty_score":0.02258754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1637705064936936,"score_gpt":0.2376760884393412,"score_spread":0.07390558194564753,"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."}}