{"id":"W4311034063","doi":"10.1145/3550469.3555388","title":"Marginal Multiple Importance Sampling","year":2022,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Sampling (signal processing); Marginal distribution; Probability density function; Importance sampling; Computer science; Mathematics; Conditional probability distribution; Algorithm; Mathematical optimization; Applied mathematics; Statistics; Random variable; Monte Carlo method","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":[0.0004135944,0.00006490292,0.00007917145,0.00003963711,0.0002302813,0.0000460245,0.0006025688,0.00001141469,0.000254696],"category_scores_gemma":[0.00001442781,0.00005989544,0.0000429189,0.0002183192,0.00000845566,0.0001454273,0.0004133935,0.0001351248,0.000008766713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003007864,"about_ca_system_score_gemma":0.00003534671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001465406,"about_ca_topic_score_gemma":0.000004284289,"domain_scores_codex":[0.9991928,0.00006243366,0.0001116082,0.0002622959,0.0001823655,0.0001884588],"domain_scores_gemma":[0.9994612,0.00006427061,0.00003537146,0.000370773,0.00001429763,0.00005405897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004782154,0.00004502222,0.003436396,0.000003675675,0.000006576214,0.00002447096,0.0002701505,0.0001646488,0.001504409,0.8523113,0.003500991,0.1387276],"study_design_scores_gemma":[0.0008409568,0.0001623611,0.006416855,0.000003737682,0.0000056035,0.0002115169,0.00006932782,0.4336056,0.001561515,0.3118359,0.2446527,0.0006339474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002274179,0.0001053295,0.9851933,0.0008216329,0.0002679236,0.00006534804,0.000001540883,0.0001377633,0.01113296],"genre_scores_gemma":[0.2129495,0.000001639942,0.7847264,0.001078868,0.00002986777,0.00002128229,9.136377e-7,0.000004162893,0.001187419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5404754,"threshold_uncertainty_score":0.2788743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03667247178179953,"score_gpt":0.2759956895467671,"score_spread":0.2393232177649675,"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."}}