{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007701784,0.0003903486,0.0005183546,0.0004216098,0.0001282689,0.0001521466,0.001505055,0.0005453286,0.000006134031],"category_scores_gemma":[0.00006936715,0.0004443132,0.0002613122,0.0007075113,0.00006972393,0.0006469039,0.001095383,0.000791773,0.000009698649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001685187,"about_ca_system_score_gemma":0.0002495617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000435833,"about_ca_topic_score_gemma":0.00008030683,"domain_scores_codex":[0.9974635,0.0002802813,0.0003239518,0.001232272,0.0001094046,0.0005906295],"domain_scores_gemma":[0.997933,0.0003164701,0.000255884,0.001019759,0.0002474333,0.0002275089],"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":[0.00003581624,0.00008877819,0.0002160302,0.00005272798,0.00002603824,0.00001013679,0.0001958372,0.526126,0.00003628964,0.4706984,0.00004770176,0.002466188],"study_design_scores_gemma":[0.0003660182,0.00002368502,0.00003722592,0.00006768257,0.00003338936,0.000002363583,0.00001091049,0.6738708,0.00007966264,0.3251486,0.00001798164,0.0003416856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02237559,0.00008702738,0.9749996,0.00007630806,0.001236436,0.0004758692,0.00001265796,0.0002760812,0.0004604156],"genre_scores_gemma":[0.8887591,0.0001875993,0.1104188,0.0001088435,0.00006927732,0.00001374195,0.00002099622,0.00002059192,0.000401031],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8663835,"threshold_uncertainty_score":0.9998009,"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."}}