{"id":"W1986432842","doi":"10.2307/3315971","title":"Monte Carlo Kalman filter and smoothing for multivariate discrete state space models","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Kalman filter; Smoothing; Ensemble Kalman filter; Monte Carlo method; State-space representation; State space; Extended Kalman filter; Latent variable; Applied mathematics; State variable; Invariant extended Kalman filter; Gaussian; Mathematics; Multivariate statistics; Alpha beta filter; Fast Kalman filter; Computer science; Algorithm; Statistics; Physics; Moving horizon estimation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005458881,0.0005361277,0.001234016,0.001263211,0.0007575271,0.001696107,0.001414359,0.001818053,0.003299059],"category_scores_gemma":[0.02191619,0.0006980843,0.001042894,0.001519404,0.001811589,0.002820205,0.001242207,0.002092741,0.000484698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002187734,"about_ca_system_score_gemma":0.001779294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01774266,"about_ca_topic_score_gemma":0.01269343,"domain_scores_codex":[0.9976132,0.001181204,0.0001124879,0.0003805469,0.0005564578,0.0001561627],"domain_scores_gemma":[0.9902712,0.00800705,0.0005840101,0.0005150689,0.0005015017,0.0001212711],"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":[0.0000485134,0.0000248808,0.0008704072,0.00005351524,0.00005158932,0.00006538379,0.0001257764,0.547685,0.0003870067,0.4248381,0.0009408771,0.02490898],"study_design_scores_gemma":[0.00001047553,0.000009235292,0.0002152144,0.00001415871,0.00001239838,0.00002310051,0.00001044901,0.8781804,0.0002037959,0.119797,0.001504958,0.00001879681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004260384,0.0002449562,0.9938204,0.000210539,0.0000276165,0.00001227816,0.00003438575,0.0001401573,0.001249407],"genre_scores_gemma":[0.4983523,0.001677282,0.4896886,0.0002185729,0.000190057,0.0002503406,0.0003882661,0.0001589481,0.009075683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01774266,"threshold_uncertainty_score":0.0352788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03366149097554022,"score_gpt":0.2453966697647787,"score_spread":0.2117351787892385,"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."}}