{"id":"W1970414145","doi":"10.1117/12.849698","title":"Feynman path integral inspired computational methods for nonlinear filtering","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Path integral formulation; Feynman diagram; Path (computing); Nonlinear system; Mathematics; Integral equation; Applied mathematics; Functional integration; Action (physics); Computer science; Mathematical analysis; Physics; Quantum mechanics; Mathematical 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":[0.0009996236,0.0006403488,0.0006981682,0.000677332,0.0005235348,0.0009639761,0.0008638675,0.001278233,0.002267562],"category_scores_gemma":[0.002381479,0.0002996201,0.0006277741,0.0007827784,0.001404971,0.001428013,0.001010111,0.001587267,0.0004329089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072402,"about_ca_system_score_gemma":0.0008694241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002332614,"about_ca_topic_score_gemma":0.001991127,"domain_scores_codex":[0.9997819,0.00009288246,0.000009507182,0.00002016526,0.00007816097,0.00001738895],"domain_scores_gemma":[0.9994436,0.0003815146,0.00003476814,0.00004971223,0.00006693597,0.00002346948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001810203,0.00002237623,0.0001641508,0.00006626477,0.00002549767,0.00003845827,0.00005908353,0.2741796,0.001165728,0.7009804,0.001167244,0.02211315],"study_design_scores_gemma":[0.000005432833,0.000004329388,0.00002428969,0.000007430988,0.000002108579,0.000008557877,0.000004680805,0.8697892,0.0001424884,0.1285189,0.001487641,0.000004895384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003500032,0.0008162368,0.9908199,0.0003133657,0.0000851465,0.00001760106,0.0000295106,0.00007987423,0.004338305],"genre_scores_gemma":[0.2812251,0.003009882,0.7016083,0.0003062242,0.0002637767,0.0003897736,0.0001439709,0.0001988164,0.01285415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002332614,"threshold_uncertainty_score":0.00778091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01984399739541525,"score_gpt":0.2613249694646185,"score_spread":0.2414809720692033,"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."}}