{"id":"W1547513508","doi":"10.48550/arxiv.1506.07603","title":"Analytic MMSE Bounds in Linear Dynamic Systems with Gaussian Mixture Noise Statistics","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Gaussian; Noise (video); Gaussian noise; Mathematics; Applied mathematics; Statistical physics; Computer science; Algorithm; Physics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004023282,0.001872763,0.001325683,0.002035346,0.0006464836,0.002546599,0.001102799,0.001416391,0.002464997],"category_scores_gemma":[0.03347991,0.0006253613,0.000797903,0.001325844,0.002461638,0.003483501,0.002392022,0.002296305,0.000675925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001992879,"about_ca_system_score_gemma":0.001166061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003544288,"about_ca_topic_score_gemma":0.002133108,"domain_scores_codex":[0.9979709,0.000636885,0.0001123065,0.0002764877,0.0007198266,0.0002835732],"domain_scores_gemma":[0.9836737,0.01326041,0.0007128929,0.0006219608,0.001606839,0.0001242278],"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":[0.00006296112,0.00002536959,0.0005729254,0.0002598032,0.00005074889,0.0001130311,0.0002353577,0.8406911,0.002716196,0.1290394,0.001272221,0.02496083],"study_design_scores_gemma":[0.000002792947,0.00002118396,0.0002239437,0.00005869189,0.00001557025,0.00004378698,0.00003502284,0.9454293,0.001388714,0.05185449,0.0009062683,0.00002024401],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007466779,0.002061497,0.984821,0.0003458466,0.00004668203,0.00001871584,0.00008396814,0.0002378152,0.004917605],"genre_scores_gemma":[0.8143092,0.006592665,0.1715145,0.000510053,0.0003709097,0.0002897388,0.0004417198,0.0002841486,0.005686984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004023282,"threshold_uncertainty_score":0.02127743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03937995577709159,"score_gpt":0.1988072643995096,"score_spread":0.1594273086224181,"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."}}