{"id":"W2139913462","doi":"10.1109/tsp.2008.929660","title":"Approximate Conditional Mean Particle Filtering for Linear/Nonlinear Dynamic State Space Models","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Else Kröner-Fresenius-Stiftung","keywords":"Particle filter; Algorithm; Recursion (computer science); Mathematics; Conditional expectation; Conditional probability distribution; Kalman filter; Moment (physics); Gaussian; Filter (signal processing); Computer science; Mathematical optimization; 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":[0.00105371,0.0005032638,0.0008313757,0.0004528873,0.0003053239,0.000663139,0.0006781986,0.0008817078,0.0009689577],"category_scores_gemma":[0.004003205,0.0003585985,0.0005770191,0.0006822037,0.0006749594,0.0009258487,0.0006020496,0.001013937,0.0002038471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000775887,"about_ca_system_score_gemma":0.000997889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009332271,"about_ca_topic_score_gemma":0.006757403,"domain_scores_codex":[0.9996606,0.00009938277,0.00001595597,0.00006917277,0.0001118295,0.00004307682],"domain_scores_gemma":[0.9990088,0.0006674672,0.00009973864,0.00009208188,0.0001105542,0.00002141943],"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":[0.00005766646,0.00001934553,0.0006278998,0.00005084701,0.00002688749,0.00006133852,0.00004754587,0.9402816,0.00132327,0.02551477,0.0006162939,0.03137254],"study_design_scores_gemma":[0.000001853741,0.000004281336,0.00008191444,0.000001404726,0.000002379189,0.000007170872,0.000001455324,0.9965467,0.0001788715,0.003015626,0.0001559256,0.000002340735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00622553,0.0001168087,0.9930384,0.00006306238,0.00001715478,0.000006830915,0.00001659516,0.0001057737,0.0004098825],"genre_scores_gemma":[0.654879,0.0007396293,0.3402171,0.0001090647,0.00009370745,0.0001060564,0.0002345041,0.00006184129,0.003559094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009332271,"threshold_uncertainty_score":0.01855588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04000679858136258,"score_gpt":0.2683401606268145,"score_spread":0.228333362045452,"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."}}