{"id":"W2962950510","doi":"10.48550/arxiv.1206.4654","title":"A Generalized Loop Correction Method for Approximate Inference in\\n Graphical Models","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Graphical model; Belief propagation; Inference; Dependency (UML); Approximate inference; Loop (graph theory); Computer science; Probabilistic logic; Algorithm; Tree (set theory); Theoretical computer science; Mathematics; Artificial intelligence","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":[0.004566853,0.001147903,0.00151449,0.001937553,0.0009351969,0.001588894,0.003276738,0.001913177,0.003863756],"category_scores_gemma":[0.0349088,0.0007238066,0.001479046,0.002303746,0.002133213,0.002630465,0.002277381,0.003155696,0.001041425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166275,"about_ca_system_score_gemma":0.002798144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009313079,"about_ca_topic_score_gemma":0.01132254,"domain_scores_codex":[0.9963535,0.001841155,0.0001313968,0.0005892725,0.0009247207,0.0001599038],"domain_scores_gemma":[0.9832775,0.01220893,0.0008006963,0.002074317,0.001398889,0.000239617],"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":[0.0001941438,0.00007775359,0.001869287,0.0002487247,0.0001799333,0.0001725215,0.0002928608,0.5510597,0.001882329,0.1785329,0.00557298,0.259917],"study_design_scores_gemma":[0.00001549596,0.00001397866,0.00007201224,0.00001299225,0.00001252263,0.00002333345,0.000006906865,0.9376758,0.000454066,0.06014193,0.00156018,0.00001085776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001167216,0.00009050844,0.9980181,0.00007655697,0.00002018109,0.00001771847,0.00003049175,0.0003260752,0.0002531064],"genre_scores_gemma":[0.1243726,0.0003867674,0.87109,0.0002875261,0.0001846605,0.0003101022,0.0003919082,0.0003600884,0.002616468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009313079,"threshold_uncertainty_score":0.0241521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1431360333571014,"score_gpt":0.2549403082738164,"score_spread":0.111804274916715,"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."}}