{"id":"W2128243427","doi":"10.1002/cjs.5550340206","title":"Approximating bayesian inference by weighted likelihood","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Bayes' theorem; Bayes factor; Inference; Estimator; Bayesian inference; Bayesian probability; Computer science; Statistics; Bayes' rule; Econometrics; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004569911,0.0001731946,0.0003725296,0.000140409,0.000157393,0.00007288048,0.0002075259,0.00008052043,0.0001849026],"category_scores_gemma":[0.001845378,0.0001574293,0.00004672214,0.0001463694,0.000119144,0.0001172049,0.000009143017,0.0003249722,0.000004140905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001377288,"about_ca_system_score_gemma":0.0006476332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001337349,"about_ca_topic_score_gemma":0.005529424,"domain_scores_codex":[0.9982695,0.00009385079,0.000802878,0.0001317804,0.0002414367,0.0004605362],"domain_scores_gemma":[0.9971892,0.001179935,0.0004819214,0.000151357,0.0003946351,0.0006029796],"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":[0.000008011943,0.00004321846,0.0005014524,0.0000939621,0.00003251914,0.0002684268,0.0002151136,0.00002863294,0.0002320823,0.8958473,0.05781158,0.04491766],"study_design_scores_gemma":[0.0003254925,0.00009869509,0.00008708204,0.00008180799,0.00005421691,0.00005258778,0.00007620979,0.005567587,0.0001937069,0.9887981,0.004471211,0.0001933422],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001290798,0.0001767405,0.995158,0.0001168604,0.0001869617,0.00009091619,0.0009574617,0.000009430438,0.002012856],"genre_scores_gemma":[0.1501535,0.000007268527,0.8493919,0.00007330824,0.0001251544,0.000001814364,0.00001612571,0.00003000898,0.0002009455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1488627,"threshold_uncertainty_score":0.6419781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03890113692298602,"score_gpt":0.3287147092780725,"score_spread":0.2898135723550865,"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."}}