{"id":"W2991602565","doi":"10.1109/smc.2019.8914630","title":"Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping","year":2019,"lang":"en","type":"article","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Variance (accounting); Markov decision process; State space; Markov chain; Transformation (genetics); Reinforcement learning; Computer science; Markov process; State (computer science); Exponential function; Markov model; Invariant (physics); Mathematical optimization; Econometrics; Mathematics; Statistics; Artificial intelligence; Algorithm; Machine learning; Economics","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":[0.003596303,0.0008616154,0.001054504,0.0007170588,0.0004501102,0.001255809,0.0008263568,0.0008070609,0.001629732],"category_scores_gemma":[0.01564167,0.0004102433,0.001031817,0.0005760583,0.001949964,0.002809808,0.002241406,0.00187155,0.0001727247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105915,"about_ca_system_score_gemma":0.001186167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002227608,"about_ca_topic_score_gemma":0.00124897,"domain_scores_codex":[0.9979296,0.00105619,0.0001093843,0.0003393528,0.0004155978,0.0001499196],"domain_scores_gemma":[0.9921462,0.006005247,0.0006871474,0.0005738961,0.000431849,0.0001556711],"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":[0.0001003404,0.0000410054,0.001172193,0.00004385853,0.00003198179,0.0000655012,0.0001165757,0.8904198,0.001327559,0.08670545,0.0001935157,0.01978226],"study_design_scores_gemma":[0.000004852021,0.00001870688,0.00008389407,0.000004543977,0.000004689613,0.00001065632,0.000005879255,0.9771931,0.0004187577,0.02216042,0.00008834097,0.000006090901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02635087,0.00006343135,0.972559,0.00009633719,0.00001043908,0.00002460513,0.00002160935,0.000120273,0.0007534865],"genre_scores_gemma":[0.8925893,0.0001153499,0.1061118,0.00004876961,0.00001742498,0.0001126889,0.00006769614,0.00005272865,0.0008841467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003596303,"threshold_uncertainty_score":0.01901931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03557821950545821,"score_gpt":0.3500579117565214,"score_spread":0.3144796922510632,"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."}}