{"id":"W1975938103","doi":"10.1016/j.sysconle.2006.06.004","title":"A note on exponential stability of the nonlinear filter for denumerable Markov chains","year":2006,"lang":"en","type":"article","venue":"Systems & Control Letters","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Markov chain; Mathematics; Countable set; Examples of Markov chains; Exponential stability; Ergodicity; Filter (signal processing); Applied mathematics; Markov chain mixing time; Markov renewal process; Nonlinear system; Markov process; Markov property; Stability (learning theory); Class (philosophy); Exponential function; Nonlinear filter; Control theory (sociology); Discrete mathematics; Mathematical analysis; Markov model; Filter design; Computer science; Statistics; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005732015,0.0009151275,0.001475341,0.001286488,0.001131549,0.002080481,0.001627994,0.00194017,0.004396617],"category_scores_gemma":[0.02549184,0.0006834076,0.002083713,0.001040715,0.003573061,0.004150375,0.002880121,0.005213738,0.0005462981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001631957,"about_ca_system_score_gemma":0.001146805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004226872,"about_ca_topic_score_gemma":0.002789495,"domain_scores_codex":[0.9984189,0.0005363499,0.00008428224,0.0003666433,0.0004568259,0.0001370382],"domain_scores_gemma":[0.9749249,0.02209166,0.0005706165,0.0009171134,0.001157296,0.0003383555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007660905,0.00001703665,0.000348767,0.0001075859,0.00007089693,0.0001706052,0.0001932153,0.07519925,0.001882869,0.9112591,0.001432608,0.009241405],"study_design_scores_gemma":[0.00001272223,0.00002095051,0.0001497842,0.00003360063,0.00002001537,0.00004667175,0.00001408041,0.369369,0.0005658426,0.6272207,0.002516164,0.00003041177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01457738,0.001917896,0.9694926,0.001777055,0.0004948414,0.00002993599,0.0001117745,0.0001174339,0.01148112],"genre_scores_gemma":[0.7643589,0.007854266,0.1893091,0.002375178,0.002588848,0.0003321581,0.0003358768,0.0004743425,0.03237129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005732015,"threshold_uncertainty_score":0.03031415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812076244011097,"score_gpt":0.2845822500862166,"score_spread":0.2564614876461056,"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."}}