{"id":"W2056727044","doi":"10.1117/12.436952","title":"&lt;title&gt;Particle filters for combined state and parameter estimation&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle filter; SIGNAL (programming language); Context (archaeology); Computer science; Probabilistic logic; Algorithm; Auxiliary particle filter; Estimation theory; Nonlinear system; Conditional probability distribution; State (computer science); Probability distribution; Sequence (biology); Mathematics; Statistics; Artificial intelligence; Kalman filter; Physics; Extended Kalman filter","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":[0.0008836658,0.00122526,0.0009696281,0.001231103,0.00056221,0.001798337,0.001339056,0.001950808,0.0368914],"category_scores_gemma":[0.00278967,0.0003686589,0.0004886879,0.002311315,0.0008460573,0.001993086,0.0007423154,0.001771004,0.02828276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009788559,"about_ca_system_score_gemma":0.0007373509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004203395,"about_ca_topic_score_gemma":0.004458229,"domain_scores_codex":[0.9993871,0.0001081243,0.00004352715,0.0001350414,0.0002814553,0.0000447823],"domain_scores_gemma":[0.9988694,0.000314535,0.0000907971,0.0002192738,0.0004480683,0.00005785688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003445323,0.00008223924,0.0005104163,0.0004259594,0.00006594712,0.0002574342,0.00008634,0.02927401,0.01009491,0.07857522,0.2202679,0.660015],"study_design_scores_gemma":[0.0001056192,0.0001487029,0.001071144,0.0002105277,0.00005392039,0.0003910355,0.00003183622,0.4305232,0.02859717,0.04715132,0.4915922,0.0001233625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00138908,0.003663933,0.9655927,0.001464595,0.003861472,0.0001376045,0.0006325959,0.003449842,0.01980814],"genre_scores_gemma":[0.08580058,0.00928167,0.6793073,0.001327533,0.003964704,0.0005085959,0.004997003,0.002467654,0.2123449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0368914,"threshold_uncertainty_score":0.123414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289560168328842,"score_gpt":0.2288022982604535,"score_spread":0.2159066965771651,"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."}}