{"id":"W2957474029","doi":"10.48550/arxiv.1907.05231","title":"Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","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); State space; Markov chain; Markov decision process; Transformation (genetics); State (computer science); Reinforcement learning; Exponential function; Markov process; Computer science; Invariant (physics); Mathematics; Econometrics; Mathematical optimization; Statistics; Artificial intelligence; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201247,0.0002415606,0.000323292,0.0008045941,0.0001784708,0.0001775353,0.0007992601,0.000191747,0.0001115342],"category_scores_gemma":[0.0002294934,0.0002238289,0.00009431584,0.002256928,0.00009856339,0.0005182248,0.0001180744,0.0004701376,0.0001097395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001838341,"about_ca_system_score_gemma":0.0004657717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003199343,"about_ca_topic_score_gemma":0.0005686592,"domain_scores_codex":[0.9979877,0.0001430091,0.0005139977,0.0008046043,0.0003033509,0.0002473584],"domain_scores_gemma":[0.9968572,0.0008918736,0.0006265223,0.0008824878,0.0006510625,0.00009086938],"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":[0.00007699386,0.00007056369,0.02229269,0.00005709647,0.0000121885,0.000005983028,0.0002729648,0.9715182,0.000005033586,0.001896841,0.0000367768,0.003754684],"study_design_scores_gemma":[0.000598354,0.00003637408,0.01423862,0.0001404379,0.00004101425,7.351696e-7,0.000152735,0.9062315,0.0002275893,0.07744974,0.000569765,0.0003131367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2504377,0.000008894159,0.7461655,0.0001001692,0.00004472589,0.0007520738,0.00008181456,0.0001193961,0.002289716],"genre_scores_gemma":[0.9931504,0.00004339907,0.006103422,0.00005855491,0.00001310849,0.000008762384,0.00006331109,0.00001790761,0.0005412035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7427127,"threshold_uncertainty_score":0.9127477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1153009802636342,"score_gpt":0.2677358387421125,"score_spread":0.1524348584784783,"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."}}