{"id":"W1705626586","doi":"10.48550/arxiv.1206.5239","title":"Large-Flip Importance Sampling","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Markov chain Monte Carlo; Sampling (signal processing); Monte Carlo method; Importance sampling; Computer science; Algorithm; Rejection sampling; Process (computing); Event (particle physics); Hybrid Monte Carlo; Statistical physics; Mathematical optimization; Mathematics; Statistics; Physics; Telecommunications","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":[0.002997824,0.0007473354,0.001073084,0.001014414,0.0007362001,0.001318986,0.002267621,0.001422278,0.006037072],"category_scores_gemma":[0.01594854,0.0006068109,0.0008114779,0.0008521533,0.001506366,0.001865099,0.002650702,0.002362526,0.001364673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009065713,"about_ca_system_score_gemma":0.001338738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774306,"about_ca_topic_score_gemma":0.003078793,"domain_scores_codex":[0.9985007,0.0007316449,0.00004574556,0.0002576976,0.0003599739,0.0001042841],"domain_scores_gemma":[0.9954885,0.002781323,0.0002374553,0.000754913,0.0004833083,0.000254452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001923194,0.0001420562,0.002941531,0.0001702082,0.0001025671,0.0002236506,0.0001916425,0.4067911,0.00361819,0.3800443,0.0078091,0.1977734],"study_design_scores_gemma":[0.00003556951,0.00002070503,0.0001794016,0.00001343767,0.000009550715,0.0000545952,0.000007037657,0.9206957,0.0007435998,0.07553437,0.002693177,0.00001283555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002189489,0.00005925961,0.9965593,0.00008961666,0.00003883721,0.00004215606,0.0000213854,0.0001760217,0.0008239172],"genre_scores_gemma":[0.1632719,0.0001917218,0.8308276,0.0003318086,0.0001887359,0.0004445818,0.0002702512,0.00027908,0.004194309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006037072,"threshold_uncertainty_score":0.02019608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2828764205792926,"score_gpt":0.2842820396739688,"score_spread":0.00140561909467618,"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."}}