{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008772095,0.001515835,0.002131346,0.002113497,0.0008573115,0.002182408,0.002830919,0.001199372,0.004941466],"category_scores_gemma":[0.0245232,0.0009486037,0.001498438,0.002138347,0.002274686,0.002769381,0.003277372,0.002720514,0.0007806824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539052,"about_ca_system_score_gemma":0.001866449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001919143,"about_ca_topic_score_gemma":0.00248383,"domain_scores_codex":[0.9922503,0.003913559,0.000213696,0.001075905,0.00219759,0.0003489036],"domain_scores_gemma":[0.9905459,0.00608575,0.0004092494,0.001330393,0.001337807,0.0002910127],"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":[0.0002850328,0.0001895605,0.003554954,0.0006165653,0.000414143,0.000281106,0.0003379442,0.1942157,0.004140828,0.5322692,0.007084919,0.25661],"study_design_scores_gemma":[0.00005588468,0.00008941663,0.0006089479,0.0001112554,0.00008948173,0.0002027988,0.00004710411,0.7385191,0.002294624,0.2487539,0.009190938,0.0000365548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001581011,0.0002361566,0.996753,0.00007935969,0.0000373706,0.00006738234,0.00002671526,0.0000983415,0.001120584],"genre_scores_gemma":[0.1766912,0.0007379545,0.8172601,0.0002903577,0.0003997969,0.0004353244,0.000306675,0.0001743988,0.003704124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008772095,"threshold_uncertainty_score":0.04639179,"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."}}