{"id":"W4389473849","doi":"10.1109/lsp.2023.3341001","title":"Population Monte Carlo With Normalizing Flow","year":2023,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Huawei Technologies (Canada)","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Monte Carlo method; Algorithm; Mathematical optimization; Sampling (signal processing); Importance sampling; Inference; Rejection sampling; Population; Markov chain; Hybrid Monte Carlo; Mathematics; Machine learning; Artificial intelligence; Statistics; Bayesian probability","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":[0.0003793665,0.0001629332,0.0001635479,0.0001671076,0.0002429659,0.0002915892,0.0003966858,0.00004783315,0.000001668162],"category_scores_gemma":[0.000004396778,0.0001329821,0.00004423436,0.000766154,0.00003093443,0.0008152073,0.00004195203,0.0001657457,0.00001753212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003484245,"about_ca_system_score_gemma":0.00003725364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006193057,"about_ca_topic_score_gemma":0.000006110959,"domain_scores_codex":[0.998642,0.00007329164,0.0001770369,0.0003922183,0.000342085,0.0003734097],"domain_scores_gemma":[0.9995165,0.00003686398,0.00009404524,0.0002219801,0.00004790289,0.00008274888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002543901,0.00002051501,0.001098175,0.0001498429,0.00002782513,0.0002111852,0.002576829,0.04927696,0.04151619,0.0005045297,0.004562221,0.9000303],"study_design_scores_gemma":[0.0002843846,0.00004194434,0.001955631,0.0001610124,0.0000130045,0.00003523935,0.000008587816,0.9927173,0.00283018,0.001322614,0.0002861686,0.0003439821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06080555,0.00005204593,0.9360762,0.002228757,0.0001613924,0.00008874608,8.345407e-7,0.0004417657,0.0001446709],"genre_scores_gemma":[0.6844345,0.000001172722,0.3135529,0.001785835,0.0001427217,0.00001104201,0.000001631208,0.00001678832,0.00005338687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9434403,"threshold_uncertainty_score":0.5422854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798211009639149,"score_gpt":0.2484016818867318,"score_spread":0.2304195717903403,"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."}}