{"id":"W2784647367","doi":"10.2172/1417145","title":"Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library","year":2018,"lang":"en","type":"report","venue":"","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Los Alamos National Laboratory; National Nuclear Security Administration; U.S. Department of Energy","keywords":"Markov chain Monte Carlo; Python (programming language); Computer science; Monte Carlo method; Bayesian probability; Metropolis–Hastings algorithm; Inference; Bayesian inference; Hybrid Monte Carlo; Markov chain; Data mining; Programming language; Machine learning; Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003950655,0.001789792,0.001648724,0.00239988,0.00103933,0.003443334,0.004564142,0.00202621,0.06010602],"category_scores_gemma":[0.01691046,0.001676509,0.001835122,0.00200934,0.001127061,0.002728324,0.003357536,0.003594371,0.03454421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152884,"about_ca_system_score_gemma":0.004189457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004764199,"about_ca_topic_score_gemma":0.009029645,"domain_scores_codex":[0.9969485,0.001033181,0.0001929373,0.0003252765,0.001343113,0.0001571186],"domain_scores_gemma":[0.9948452,0.002877816,0.0003808647,0.0008576055,0.0008018783,0.0002368179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005166921,0.0003682486,0.003462178,0.001414545,0.0006016386,0.0004735632,0.0003063683,0.1326749,0.005969376,0.1806738,0.2934198,0.3801188],"study_design_scores_gemma":[0.0002365242,0.0000524168,0.0006037405,0.0002525701,0.00006830614,0.0003112064,0.00002822647,0.615624,0.008740409,0.1821468,0.1918142,0.0001217706],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0004653153,0.000216784,0.9536363,0.0002299713,0.00009003136,0.0001524738,0.00385208,0.03684787,0.004509192],"genre_scores_gemma":[0.01532243,0.0005122962,0.9547057,0.0003952588,0.0001608328,0.0009765035,0.007829555,0.01202509,0.008072416],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06010602,"threshold_uncertainty_score":0.2010747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0829649898829235,"score_gpt":0.3817781791954628,"score_spread":0.2988131893125393,"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."}}