{"id":"W2152650468","doi":"10.1109/9.898698","title":"A probabilistic analysis of bias optimality in unichain Markov decision processes","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Markov decision process; Probabilistic logic; Markov process; Mathematical optimization; State space; Markov chain; Partially observable Markov decision process; Decision theory; Computer science; Mathematics; Markov model; Optimal decision; Value (mathematics); Decision problem; Markov kernel; Variable-order Markov model; Artificial intelligence; Algorithm; Statistics; Decision tree","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":[0.002072195,0.000269167,0.0009976138,0.0009213016,0.0000727288,0.00002894587,0.0002460681,0.0001419744,0.0002420119],"category_scores_gemma":[0.001148838,0.0002277557,0.0003580917,0.002553976,0.00007966326,0.00009612627,0.000001747209,0.0001730497,3.607507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393183,"about_ca_system_score_gemma":0.0001451924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046624,"about_ca_topic_score_gemma":0.002550061,"domain_scores_codex":[0.9973397,0.0004829221,0.001033707,0.0003747204,0.0004501517,0.0003188084],"domain_scores_gemma":[0.9929972,0.00568187,0.0003016813,0.0006782504,0.0002326651,0.0001083177],"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":[0.002357942,0.009534644,0.002581017,0.003675093,0.005877964,0.0001487247,0.005657289,0.2240851,0.002537241,0.002632489,0.000172546,0.7407399],"study_design_scores_gemma":[0.002921257,0.0001790598,0.001046157,0.0003711046,0.002270466,0.00001117329,0.0002976526,0.9888111,0.0005917626,0.003103806,0.00005065449,0.0003457732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4043755,0.00003080172,0.594332,0.00008875004,0.00007556143,0.0005893231,0.00004090845,0.00008783259,0.0003792848],"genre_scores_gemma":[0.9659389,0.00003679561,0.03351171,0.00005386085,0.000009190793,0.0002086243,0.000001657153,0.0000262107,0.0002131026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.764726,"threshold_uncertainty_score":0.9287608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701441502884771,"score_gpt":0.3422949579532089,"score_spread":0.2852805429243612,"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."}}