{"id":"W2113347847","doi":"10.1109/acc.2006.1657187","title":"Gradient-free maximum likelihood parameter estimation with particle filters","year":2006,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Likelihood function; Particle filter; Gaussian; Algorithm; Computer science; Mathematical optimization; Estimation theory; Applied mathematics; Mathematics; Kalman filter; Artificial intelligence; 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":[0.001403377,0.0007061451,0.0008650565,0.0005851876,0.0002886662,0.0007290975,0.00104728,0.001349136,0.0007316261],"category_scores_gemma":[0.005914429,0.0006325205,0.0005414555,0.0006567035,0.0007683606,0.001615492,0.0008317188,0.001167757,0.0004721351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005082934,"about_ca_system_score_gemma":0.0008850849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003403281,"about_ca_topic_score_gemma":0.002543901,"domain_scores_codex":[0.9993439,0.0002347125,0.00002469648,0.00008605635,0.0002788295,0.00003175125],"domain_scores_gemma":[0.9982582,0.001287831,0.0001247849,0.0001505797,0.0001530013,0.00002557419],"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":[0.0001187303,0.00004955515,0.0005326868,0.00008007592,0.00006684839,0.00005958879,0.00008377122,0.8966179,0.004419895,0.01486607,0.0007156171,0.08238916],"study_design_scores_gemma":[0.00001106904,0.0000127129,0.00007793396,0.000002891539,0.000003221797,0.000012952,0.000002260244,0.9944607,0.001227185,0.003831476,0.0003520637,0.000005497411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003198582,0.0000572878,0.996143,0.00003876426,0.00001040679,0.00001013666,0.000007077078,0.0002150251,0.000319668],"genre_scores_gemma":[0.364597,0.0001988591,0.6325455,0.00009716938,0.00004799936,0.0001190412,0.0001103752,0.0001311103,0.002152997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003403281,"threshold_uncertainty_score":0.007421851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008236925530937134,"score_gpt":0.201790695129133,"score_spread":0.1935537695981959,"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."}}