{"id":"W2884182684","doi":"10.1109/lcomm.2018.2856746","title":"Bayesian MMSE Estimation of a Gaussian Source in the Presence of Bursty Impulsive Noise","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Minimum mean square error; Estimator; Maximum a posteriori estimation; Computer science; Gaussian noise; Noise (video); Algorithm; Bayesian probability; Statistics; Mean squared error; Mathematics; Gaussian; Artificial intelligence; Maximum likelihood; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009985118,0.0005469415,0.0007862681,0.0003212166,0.0001513751,0.0007647127,0.000600735,0.0008089303,0.000407522],"category_scores_gemma":[0.006035338,0.0003873587,0.0003050257,0.0003737149,0.0006165865,0.001174779,0.0007558949,0.0006955591,0.0001748527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003596799,"about_ca_system_score_gemma":0.0007497697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001745555,"about_ca_topic_score_gemma":0.001666352,"domain_scores_codex":[0.9995565,0.0001317833,0.00002895678,0.00007866952,0.0001651174,0.00003893236],"domain_scores_gemma":[0.998635,0.0009439307,0.0001443635,0.00007310978,0.0001797208,0.00002377494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001614099,0.00002773837,0.002165963,0.0001617619,0.00008895066,0.0002227289,0.0001306838,0.8872176,0.0130324,0.04261938,0.0005804088,0.05359095],"study_design_scores_gemma":[0.000004426931,0.00001678195,0.0003432191,0.00001009996,0.00001185385,0.00005100548,0.000009994061,0.9907239,0.002159664,0.006434788,0.0002266408,0.000007634295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01945958,0.0001819266,0.9795057,0.0001039243,0.000008992319,0.000006517621,0.00003235017,0.00007560584,0.0006254721],"genre_scores_gemma":[0.8559721,0.0008025732,0.1405035,0.0001258962,0.00006479776,0.00004137449,0.0001754487,0.00004364649,0.002270802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001745555,"threshold_uncertainty_score":0.005280674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535738816073991,"score_gpt":0.2638656284768554,"score_spread":0.2485082403161154,"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."}}