{"id":"W3106259297","doi":"","title":"1Nonanticipative Rate Distortion Function and Relations to Filtering Theory","year":2016,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematics; Infimum and supremum; Realizability; Realization (probability); RDF; Rate of convergence; Conditional probability distribution; Relation (database); Distribution (mathematics); Convergence (economics); Mathematical optimization; Applied mathematics; Distortion (music); Conditional expectation; Function (biology); Distribution function; Algorithm; Mathematical analysis; Computer science; Artificial intelligence; Channel (broadcasting); Data mining; Statistics","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":[0.004095625,0.001179528,0.0008791978,0.001276134,0.0005740274,0.003105283,0.001250087,0.001568538,0.003702805],"category_scores_gemma":[0.01123837,0.0004259711,0.0008374366,0.001499668,0.002574991,0.004404933,0.00164742,0.002237907,0.0008344011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002010958,"about_ca_system_score_gemma":0.0007114659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007639676,"about_ca_topic_score_gemma":0.0003596924,"domain_scores_codex":[0.9978415,0.0008488278,0.0001367678,0.0003987591,0.0005930476,0.0001809884],"domain_scores_gemma":[0.9937604,0.003874209,0.00060976,0.000591078,0.001016638,0.000147902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004058354,0.00001664975,0.0002414122,0.0000776382,0.00001624461,0.0001156838,0.00005183126,0.02222537,0.002348518,0.9639536,0.0007966378,0.01011586],"study_design_scores_gemma":[0.00001050273,0.00007000437,0.0004182986,0.00003661817,0.00001413424,0.0004099568,0.00003078743,0.3334738,0.002819766,0.6584775,0.004197335,0.00004124122],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01005204,0.00161641,0.9752873,0.0007374164,0.0001325696,0.00002390042,0.0001140612,0.00008353472,0.01195277],"genre_scores_gemma":[0.7734286,0.004125766,0.201255,0.0004877747,0.0007669505,0.000175094,0.000470277,0.0002108058,0.01907979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004095625,"threshold_uncertainty_score":0.02166003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403503867750855,"score_gpt":0.2289556906337913,"score_spread":0.2149206519562827,"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."}}