{"id":"W3103402818","doi":"10.1109/ai4i49448.2020.00024","title":"Variational learning of a shifted scaled Dirichlet model with component splitting approach","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Agence Nationale de la Recherche","keywords":"Component (thermodynamics); Inference; Focus (optics); Computer science; Dirichlet distribution; Data modeling; Artificial intelligence; Machine learning; Mixture model; Algorithm; Data mining; 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":[0.002741742,0.000842884,0.001501622,0.0008861776,0.0004867614,0.00117715,0.002456759,0.001509181,0.003222218],"category_scores_gemma":[0.007005464,0.0009198446,0.00139197,0.0009831064,0.001522141,0.001885172,0.001851852,0.002063746,0.0006607635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066473,"about_ca_system_score_gemma":0.001274138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00708156,"about_ca_topic_score_gemma":0.008262637,"domain_scores_codex":[0.9986616,0.000747345,0.00005079528,0.0002613786,0.0001835265,0.00009520918],"domain_scores_gemma":[0.9975999,0.001806651,0.0001078868,0.0001627888,0.0002343147,0.00008847267],"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":[0.0001684539,0.00005384737,0.001375709,0.0001382268,0.000124741,0.000116395,0.0002350207,0.8041437,0.002352909,0.1209894,0.002466065,0.06783555],"study_design_scores_gemma":[0.00000547128,0.000007288779,0.00007226862,0.00000544079,0.000006082608,0.00001018891,0.00000680573,0.9805661,0.0001595867,0.01881398,0.0003402086,0.000006427664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007668144,0.0002433263,0.991062,0.0001793308,0.00002769002,0.0000229106,0.00006099684,0.00009946761,0.000636152],"genre_scores_gemma":[0.5674686,0.0009788532,0.4207147,0.0004323622,0.0002051961,0.000360755,0.001045735,0.0002848358,0.008509037],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00708156,"threshold_uncertainty_score":0.0144999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540122367257212,"score_gpt":0.231335941868955,"score_spread":0.2059347181963829,"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."}}