{"id":"W1571155587","doi":"10.48550/arxiv.1301.6749","title":"Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Inference; Computer science; Bayesian network; Bayesian inference; Artificial intelligence; Flexibility (engineering); Bayesian probability; Fiducial inference; Machine learning; Bayesian statistics; Theoretical computer science; Algorithm; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001255961,0.000160114,0.0001462564,0.0001515722,0.0001291141,0.0001446142,0.0003727348,0.00009372074,0.00001604343],"category_scores_gemma":[0.00002125352,0.0001521896,0.00002141683,0.0007144949,0.00008215113,0.001086793,0.000133586,0.0002231803,0.00002158574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005737294,"about_ca_system_score_gemma":0.00005970621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004631201,"about_ca_topic_score_gemma":0.0002853685,"domain_scores_codex":[0.998888,0.00008447347,0.0001301866,0.0005497808,0.00006300885,0.0002845959],"domain_scores_gemma":[0.9992271,0.0000839703,0.00007498769,0.0003528387,0.0001256788,0.0001354259],"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":[0.00008952559,0.0002659854,0.07830285,0.00004207885,0.00004435952,0.0001500081,0.001066665,0.6072833,0.000541735,0.2863896,0.00005838699,0.02576554],"study_design_scores_gemma":[0.0005108128,0.0001111111,0.0318822,0.0000419375,0.000005759866,0.000005397445,0.00006043063,0.9574553,0.00005133196,0.009658705,0.000005708628,0.0002112767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2975758,0.00001162896,0.7015675,0.00008615307,0.00003680268,0.0001657551,2.452215e-7,0.0001041557,0.0004519426],"genre_scores_gemma":[0.9948997,0.00003233567,0.004679637,0.0000966281,0.00001781611,0.000002978571,0.0000017999,0.000007784418,0.0002613283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6973239,"threshold_uncertainty_score":0.620611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03133711301611884,"score_gpt":0.1703446384887132,"score_spread":0.1390075254725944,"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."}}