{"id":"W3010992090","doi":"10.1109/camsap45676.2019.9022674","title":"Blind Maximum Likelihood Jade in Multipath Environement Using Importance Sampling","year":2019,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Cramér–Rao bound; Computer science; Maximum likelihood; Multipath propagation; Algorithm; Maximization; Sampling (signal processing); Upper and lower bounds; Joint (building); Estimation theory; Expectation–maximization algorithm; Importance sampling; Signal-to-noise ratio (imaging); SIGNAL (programming language); Mathematical optimization; Iterative method; Statistics; Mathematics; Telecommunications; Monte Carlo method; Detector; Channel (broadcasting); Engineering","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":[0.0003381017,0.000107561,0.0001602837,0.0001930274,0.00002560347,0.00004445902,0.0004038494,0.00005188168,0.00008427168],"category_scores_gemma":[0.00002752212,0.0001062459,0.00004024382,0.0003356253,0.00001718287,0.0004925958,0.0001721281,0.00009157167,0.00003400907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000941524,"about_ca_system_score_gemma":0.00005094524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001912741,"about_ca_topic_score_gemma":0.00002508657,"domain_scores_codex":[0.9988521,0.000024854,0.0003645184,0.000312756,0.0002391502,0.0002065697],"domain_scores_gemma":[0.9992642,0.00005083686,0.0001487275,0.0004562556,0.00003559044,0.00004441117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004105239,0.0008353828,0.4154786,0.0001919567,0.00004236069,0.00001266446,0.002322969,0.009391242,0.273978,0.03793262,0.0001165921,0.2596566],"study_design_scores_gemma":[0.001263876,0.0001433423,0.05712894,0.0001809964,0.000005927845,0.00001160729,0.00009011848,0.7327869,0.1848156,0.02242965,0.0006252553,0.0005178364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3979714,0.00002283739,0.5998144,0.00005394844,0.000116716,0.0002290546,5.688704e-7,0.0001252697,0.0016658],"genre_scores_gemma":[0.5585865,0.000004721371,0.4413016,0.00005727006,0.000006661277,0.000006341208,6.824957e-7,0.000005391111,0.00003081981],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7233956,"threshold_uncertainty_score":0.4332583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03499129999447652,"score_gpt":0.2973486051363584,"score_spread":0.2623573051418818,"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."}}