{"id":"W2118943752","doi":"","title":"Measure valued differentiation for stochastic processes : the finite horizon case","year":2000,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Markov chain; Mathematics; Measure (data warehouse); Estimator; Markov kernel; Kernel (algebra); Generality; Applied mathematics; Markov process; Markov model; Continuous-time Markov chain; Mathematical optimization; Markov property; Variable-order Markov model; Discrete mathematics; Computer science; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006659123,0.0001966646,0.0002339151,0.00002514735,0.0004734451,0.000142555,0.0005199101,0.0000470659,0.00004614894],"category_scores_gemma":[0.0004991934,0.0001249942,0.0000256405,0.000155197,0.00006934342,0.0001400132,0.0001449529,0.000157322,0.000003227214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006635468,"about_ca_system_score_gemma":0.00002607069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009274863,"about_ca_topic_score_gemma":0.0006852871,"domain_scores_codex":[0.9986416,0.0002129,0.0002815571,0.0003867455,0.0001762212,0.0003009397],"domain_scores_gemma":[0.9942924,0.004818376,0.0001051855,0.0006391023,0.0000569563,0.00008798785],"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":[0.0006074695,0.00031596,0.0006594567,0.004085279,0.0003121084,0.00004967796,0.01324974,0.001000159,0.0001283901,0.0176115,0.0008338829,0.9611464],"study_design_scores_gemma":[0.0005022408,0.0001767207,0.0009418275,0.000546357,0.0003240404,0.0001009238,0.0005873627,0.8612445,0.00001228163,0.1347982,0.0004597183,0.0003058882],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1810889,0.0001635033,0.8170528,0.00009408342,0.00008170648,0.0004288353,0.0007755495,0.00007141828,0.0002431948],"genre_scores_gemma":[0.9504367,0.00005019781,0.04850428,0.0001112141,0.0003159023,0.00007292058,0.0003625053,0.00003311091,0.0001131642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9608405,"threshold_uncertainty_score":0.5097116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0788268776915495,"score_gpt":0.3330450772707143,"score_spread":0.2542181995791647,"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."}}