{"id":"W4417285887","doi":"10.48550/arxiv.2503.11583","title":"A Unified Framework for Multiple-Try Metropolis: Construction and Empirical Benchmarks","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rejection sampling; Markov chain Monte Carlo; Metropolis–Hastings algorithm; Monte Carlo method; Kernel (algebra); Markov chain; Generalization; Sampling (signal processing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01594954,0.001439727,0.001882268,0.003272163,0.001865143,0.003766761,0.005169114,0.00304083,0.007206795],"category_scores_gemma":[0.08268646,0.0008614094,0.001122331,0.003312665,0.002580536,0.004976852,0.004170841,0.004307305,0.001190126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003391285,"about_ca_system_score_gemma":0.00426862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006988034,"about_ca_topic_score_gemma":0.006569693,"domain_scores_codex":[0.9939147,0.003796964,0.0002348355,0.0005227047,0.001212284,0.0003184763],"domain_scores_gemma":[0.9743341,0.01810191,0.001006113,0.003691343,0.002313926,0.0005527089],"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":[0.0001044931,0.0001960961,0.002675474,0.0001643391,0.00005473585,0.000119402,0.0002052209,0.3614687,0.0009046738,0.5993543,0.003163424,0.03158914],"study_design_scores_gemma":[0.00002198767,0.00002640504,0.0001333428,0.00002299921,0.000005896426,0.00003079552,0.00002691269,0.9157076,0.0004478656,0.08237118,0.001191651,0.00001330863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01015794,0.0002601644,0.9831991,0.0003483473,0.00003604837,0.0002103709,0.0001748921,0.0007274014,0.00488585],"genre_scores_gemma":[0.2933056,0.0004831174,0.7016764,0.0001602715,0.0001008085,0.001296252,0.0007538351,0.0007209421,0.00150286],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01594954,"threshold_uncertainty_score":0.08435017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1729935691726951,"score_gpt":0.4333665290958567,"score_spread":0.2603729599231616,"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."}}