{"id":"W4393092636","doi":"10.1080/03610918.2024.2330709","title":"Bayesian inference of a queueing system with short- or long-tailed distributions based on Hamiltonian Monte Carlo","year":2024,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Hybrid Monte Carlo; Monte Carlo method; Statistical physics; Bayesian inference; Inference; Queueing theory; Bayesian probability; Markov chain Monte Carlo; Computer science; Applied mathematics; Mathematics; Physics; Statistics; Artificial intelligence","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":[0.0003986828,0.0001602552,0.0002182473,0.0003018294,0.0001928666,0.0002205775,0.0004059522,0.0000689297,0.000001893576],"category_scores_gemma":[0.0001009433,0.0001361055,0.00002500054,0.0007981599,0.0001133076,0.0002552891,0.0001116361,0.0002147405,0.000001306413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226209,"about_ca_system_score_gemma":0.0002032052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006137811,"about_ca_topic_score_gemma":0.0003224836,"domain_scores_codex":[0.9985196,0.000339661,0.0004613869,0.0003056187,0.0002241043,0.0001496507],"domain_scores_gemma":[0.9969283,0.001939243,0.0001019968,0.0007334407,0.0002279183,0.00006911809],"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":[0.00002704658,0.00008011459,0.001265108,0.000168907,0.00001589378,0.00001143107,0.0007425733,0.5563641,0.000006241076,0.3108799,0.0000104715,0.1304282],"study_design_scores_gemma":[0.0002646151,0.0001260797,0.007568121,0.0005573165,0.00002026998,0.000003828903,0.00003788201,0.9875258,0.000007835653,0.003692656,0.00004154179,0.0001540658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001130402,0.0001705406,0.9974983,0.0002357781,0.00006713805,0.0003496868,0.00009650669,0.0001389189,0.0003127364],"genre_scores_gemma":[0.5927851,0.00001273017,0.4070855,0.0000198689,0.000004880506,0.00002462738,0.00005005439,0.000008051192,0.000009199077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5916547,"threshold_uncertainty_score":0.555022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09033911065417165,"score_gpt":0.4157568528536674,"score_spread":0.3254177421994957,"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."}}