{"id":"W6990973618","doi":"","title":"Estimating the structure of probabilistic graphical models through a Gaussian copula with discrete marginals","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphical model; Probabilistic logic; Gaussian; Copula (linguistics); Statistical model; Probability distribution","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005774148,0.0008965613,0.00100768,0.0002001812,0.0008413958,0.0002844947,0.002654403,0.0006769174,0.00002791566],"category_scores_gemma":[0.0002305672,0.0006082355,0.0003041365,0.001031896,0.0001766315,0.001439983,0.0002695564,0.002048521,0.00001825229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001620722,"about_ca_system_score_gemma":0.0001934151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003370454,"about_ca_topic_score_gemma":0.0003982646,"domain_scores_codex":[0.994908,0.0004422886,0.001015174,0.001490575,0.001337775,0.0008061946],"domain_scores_gemma":[0.9959485,0.0003048211,0.001015425,0.00193809,0.0005818263,0.0002113451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001280024,0.00007769604,0.00001430275,0.0007775792,0.0001824273,0.00002556654,0.000101646,0.02649087,0.00310454,0.9531293,0.000002051065,0.01596604],"study_design_scores_gemma":[0.0007150198,0.0004606633,0.0002135646,0.002263616,0.0002803865,0.00009089756,0.0001424852,0.1618346,0.006609648,0.8258363,0.0001488474,0.001403994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570956,0.0004308595,0.01503075,0.0001743904,0.00176219,0.002390684,0.001097023,0.0005415354,0.02147695],"genre_scores_gemma":[0.9444966,0.00002154463,0.05442613,0.0001620353,0.00003058778,0.00004803978,0.0002207616,0.0001087936,0.0004855311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1353437,"threshold_uncertainty_score":0.9996369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208077585308968,"score_gpt":0.2570292302966235,"score_spread":0.2362214717657267,"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."}}