{"id":"W4384296658","doi":"10.32920/23688708","title":"An Advanced Statistical Method for Point Process Modelling With Missing Event Histories","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Missing data; Markov chain Monte Carlo; Computer science; Point estimation; Event (particle physics); Monte Carlo method; Econometrics; Point process; Rare events; Process (computing); Statistics; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005987957,0.00121368,0.00130513,0.002178486,0.0005960549,0.001407671,0.002405165,0.0015189,0.00331184],"category_scores_gemma":[0.01683328,0.0009236614,0.00247693,0.002132948,0.001561963,0.002140861,0.001927989,0.00365978,0.001050324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000704167,"about_ca_system_score_gemma":0.001859204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002165142,"about_ca_topic_score_gemma":0.001499148,"domain_scores_codex":[0.9966751,0.001699935,0.0002090959,0.0004595438,0.0008689453,0.00008726961],"domain_scores_gemma":[0.9902553,0.007167691,0.0006839984,0.0008999328,0.0008776743,0.0001153978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007699269,0.00006459816,0.001855707,0.0004015287,0.0002568367,0.0002999361,0.0002679484,0.5015566,0.005120343,0.346715,0.001861882,0.1415226],"study_design_scores_gemma":[0.000009903579,0.00004097331,0.0002141916,0.00003525431,0.00002422085,0.00009859612,0.00001149483,0.9262038,0.001032773,0.06697368,0.005327137,0.00002791783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002184795,0.00003639947,0.9995448,0.00001714116,0.00001109249,0.00001033508,0.00001799133,0.00005293042,0.00009084534],"genre_scores_gemma":[0.0561114,0.0005900908,0.9399643,0.0001005378,0.0002015293,0.0005056271,0.0003704143,0.0001746759,0.001981419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005987957,"threshold_uncertainty_score":0.03166771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1817028435562654,"score_gpt":0.4451761291082638,"score_spread":0.2634732855519983,"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."}}