{"id":"W2922253549","doi":"","title":"Vine copula structure learning via Monte Carlo tree search","year":2019,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Vine copula; Monte Carlo tree search; Computer science; Copula (linguistics); Monte Carlo method; Tree structure; Tree (set theory); Sampling (signal processing); Artificial intelligence; Bivariate analysis; Vine; Machine learning; Mathematics; Algorithm; Statistics; Econometrics; Binary tree","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":[0.002130706,0.001161659,0.002290659,0.001534641,0.0007589155,0.001150868,0.001797131,0.001712059,0.004424837],"category_scores_gemma":[0.009852847,0.0009804608,0.001212029,0.001632996,0.0009959575,0.001972161,0.00158067,0.00252907,0.0008025835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001269312,"about_ca_system_score_gemma":0.00269041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01126126,"about_ca_topic_score_gemma":0.01378462,"domain_scores_codex":[0.9989889,0.0004485358,0.00004555482,0.000208969,0.0001883228,0.0001196484],"domain_scores_gemma":[0.9941843,0.004478721,0.000304637,0.0003114421,0.0005150188,0.0002058791],"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":[0.00006339308,0.0000650108,0.001070046,0.00006372597,0.00006603495,0.00005052792,0.00004250201,0.9217476,0.0003608092,0.01511676,0.002550044,0.05880357],"study_design_scores_gemma":[0.000005049158,0.000005139711,0.00002917599,0.000002601142,0.00000249818,0.000004366737,0.000001965911,0.9969777,0.00004764383,0.002794228,0.000127979,0.000001702117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01319608,0.000506704,0.9837316,0.000227854,0.00003846723,0.00006980212,0.00008342166,0.0005931076,0.001552923],"genre_scores_gemma":[0.5165613,0.0005979082,0.4772241,0.0004800369,0.0001121988,0.0003900248,0.0007839735,0.0003350502,0.003515471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01126126,"threshold_uncertainty_score":0.02239144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07554591244505358,"score_gpt":0.3394016780820889,"score_spread":0.2638557656370353,"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."}}