{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003543215,0.000849858,0.00112914,0.001243815,0.000698999,0.001346455,0.001757313,0.001386077,0.002826916],"category_scores_gemma":[0.01271069,0.0006963726,0.0008422671,0.001417637,0.001639524,0.002006816,0.001478888,0.002155277,0.0008036144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001340117,"about_ca_system_score_gemma":0.002068204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006363938,"about_ca_topic_score_gemma":0.004054598,"domain_scores_codex":[0.9984421,0.0007595438,0.00004959568,0.0002503246,0.0003974064,0.0001010381],"domain_scores_gemma":[0.9955064,0.003141903,0.0002532983,0.0003704338,0.0005962406,0.0001317491],"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":[0.00006585799,0.00003985353,0.0007033822,0.00004976022,0.00002834511,0.00006842102,0.00008701059,0.8246158,0.0007797196,0.1179011,0.001275598,0.05438514],"study_design_scores_gemma":[0.000007875307,0.000006506192,0.00003664058,0.00000330472,0.000002886191,0.00001090236,0.000002447732,0.986941,0.0002339829,0.01212638,0.000623491,0.000004550517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002903297,0.00008001907,0.9956979,0.00005493136,0.00001916524,0.00004258402,0.00001541387,0.0001785447,0.001008139],"genre_scores_gemma":[0.2243261,0.0004157318,0.769574,0.0002253842,0.0001276987,0.0004529933,0.0002569945,0.0001817277,0.004439385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006363938,"threshold_uncertainty_score":0.01873851,"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."}}