{"id":"W2154728192","doi":"10.1109/ssp.2005.1628629","title":"Approximate conditional mean particle filter","year":2005,"lang":"en","type":"article","venue":"IEEE/SP 13th Workshop on Statistical Signal Processing, 2005","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Particle filter; Autoregressive model; Auxiliary particle filter; Filter (signal processing); State space; Algorithm; Conditional expectation; Filtering problem; Nonlinear system; Computer science; State-space representation; Nonlinear filter; Gaussian; Noise (video); Gaussian noise; Gaussian process; Bayesian probability; Applied mathematics; Mathematics; Control theory (sociology); Kalman filter; Filter design; Artificial intelligence; Ensemble Kalman filter; Statistics; Extended Kalman filter; Physics","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":[0.00137238,0.0006527606,0.001433842,0.0007886419,0.0004506127,0.001153737,0.001438444,0.001678891,0.001961689],"category_scores_gemma":[0.006755809,0.0005676532,0.0008643484,0.001188411,0.0007983798,0.002045869,0.0009142273,0.001374073,0.0005697175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042816,"about_ca_system_score_gemma":0.001664244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027888,"about_ca_topic_score_gemma":0.007426376,"domain_scores_codex":[0.9991703,0.000210221,0.00003319293,0.0002085253,0.0002982302,0.00007951612],"domain_scores_gemma":[0.9982579,0.0009787467,0.00012395,0.0002344972,0.000367533,0.00003731277],"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":[0.0001277743,0.00003997943,0.0009404652,0.0001049287,0.00009691244,0.00008433899,0.00007472511,0.8217656,0.002535984,0.06170645,0.002974206,0.1095486],"study_design_scores_gemma":[0.000005500012,0.000007142533,0.0001242507,0.000003520199,0.000007114802,0.00001494837,0.000002033076,0.9934853,0.0003436174,0.005420824,0.0005797852,0.000005899436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002557034,0.0001030058,0.9964478,0.00007355637,0.00003653675,0.00000906344,0.00003534504,0.0001762888,0.0005612664],"genre_scores_gemma":[0.3749717,0.0007075003,0.6171287,0.000280478,0.0001791563,0.0001812569,0.0005419652,0.0001248614,0.005884267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01027888,"threshold_uncertainty_score":0.02043808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02783973957926186,"score_gpt":0.2859967218059901,"score_spread":0.2581569822267283,"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."}}