{"id":"W2951235487","doi":"","title":"Value Elimination: Bayesian Inference via Backtracking Search","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 Toronto","funders":"","keywords":"Backtracking; Bayes' theorem; Inference; Computer science; Beam stack search; Context (archaeology); Variable elimination; Bayesian probability; Bayesian inference; Search algorithm; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Beam search; Incremental heuristic search","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":[0.003213173,0.001133846,0.001328998,0.001917826,0.0009772545,0.002603357,0.003478646,0.002068405,0.01103226],"category_scores_gemma":[0.01696099,0.001114514,0.001355696,0.002292717,0.00185875,0.003255753,0.002598629,0.003261733,0.002844518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401682,"about_ca_system_score_gemma":0.003301368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006402364,"about_ca_topic_score_gemma":0.01268358,"domain_scores_codex":[0.9968674,0.001392454,0.0001090295,0.0004494734,0.0009266068,0.0002552187],"domain_scores_gemma":[0.9935528,0.004914049,0.0002568369,0.0006120691,0.000553482,0.0001107948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003241219,0.0001775197,0.001872126,0.0004131362,0.0002523724,0.0002946632,0.0002900284,0.1777298,0.003391952,0.3309213,0.02625225,0.4580806],"study_design_scores_gemma":[0.00008796146,0.00002725155,0.0001565763,0.0000624922,0.00004958498,0.00009098442,0.00002983707,0.6895101,0.003115842,0.2980205,0.008817142,0.00003182112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001610893,0.0001631383,0.9941801,0.0002432453,0.00004636223,0.0000509531,0.000126586,0.001267013,0.00231176],"genre_scores_gemma":[0.1264038,0.0004241943,0.8657308,0.0005974552,0.0001112046,0.0002752716,0.0007119642,0.000695928,0.005049442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01103226,"threshold_uncertainty_score":0.03690654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193623849380879,"score_gpt":0.2327183567883872,"score_spread":0.1133559718502993,"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."}}