{"id":"W7091187492","doi":"","title":"Reinforced sequential Monte Carlo for amortised sampling","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institute for Advanced Research","keywords":"Monte Carlo method; Importance sampling; Sampling (signal processing); Stability (learning theory); Particle filter; Reinforcement learning; Probabilistic logic; Markov chain Monte Carlo; Sampling distribution","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003965107,0.000110331,0.000165845,0.0001170937,0.0006352839,0.00008219,0.0002893062,0.0001103799,0.00008669864],"category_scores_gemma":[0.0001593634,0.0001052183,0.000149874,0.00040346,0.00016733,0.0001650675,0.00004553113,0.00009793782,0.00001239389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003323693,"about_ca_system_score_gemma":0.0001880056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002452065,"about_ca_topic_score_gemma":0.004721868,"domain_scores_codex":[0.9988852,0.00005545819,0.0002306054,0.0002722476,0.0001793465,0.0003772041],"domain_scores_gemma":[0.9994313,0.0001249495,0.00007433799,0.0001844841,0.00009828504,0.00008666551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001035336,0.00004093916,0.8673961,0.00006669493,0.0002631962,0.000004027777,0.01271557,0.0002717171,0.0013914,0.07340405,0.006324701,0.03801806],"study_design_scores_gemma":[0.002036104,0.0001293583,0.6337937,0.0001353263,0.0002077431,4.580936e-7,0.01113577,0.000588101,0.001295042,0.007426053,0.3425419,0.0007104572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669845,0.0002266938,0.003214296,0.001657017,0.001017523,0.0003083145,0.00001370836,0.0001620178,0.02641599],"genre_scores_gemma":[0.9844604,0.00008156722,0.0003865807,0.0003297475,0.0002000671,0.00005827965,0.000006424152,0.000006887943,0.01447009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3362172,"threshold_uncertainty_score":0.4886155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1326719097293433,"score_gpt":0.3828182876378631,"score_spread":0.2501463779085198,"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."}}