{"id":"W4384296835","doi":"10.32920/23688708.v1","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); Econometrics; Point process; Monte Carlo method; Rare events; Process (computing); Estimation; Novelty; Statistics; Mathematics; Machine learning; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004139679,0.0003983832,0.0007554968,0.0002593029,0.000204835,0.000362384,0.0009645429,0.0002351423,0.000059392],"category_scores_gemma":[0.001906772,0.0002592133,0.0001146985,0.0002956363,0.00009262271,0.0002068022,0.0001680983,0.0004067175,0.00001264923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001986532,"about_ca_system_score_gemma":0.0006180317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004218124,"about_ca_topic_score_gemma":0.00001549061,"domain_scores_codex":[0.9958369,0.0001556876,0.0009214513,0.001369508,0.001264589,0.0004518442],"domain_scores_gemma":[0.9941769,0.00341846,0.000311018,0.0009846217,0.0008329311,0.0002760816],"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":[0.0001360322,0.00004651655,0.000007293116,0.0001783903,0.00002690341,0.000008397407,0.0007479006,0.963679,0.00001827741,0.02327687,0.0006527036,0.01122172],"study_design_scores_gemma":[0.0001694806,0.0001178249,0.000005346526,0.000094951,0.00003836662,0.000003477339,0.0004977991,0.6687664,0.00008359341,0.3287668,0.001157629,0.0002982976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001125804,0.00008632118,0.9967214,0.0006052624,0.0008348227,0.0008869873,0.0001325046,0.0003952299,0.00022486],"genre_scores_gemma":[0.04176321,0.000003776771,0.955789,0.00003983519,0.0001300107,0.0002828784,0.00005815054,0.00007674633,0.001856376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3054899,"threshold_uncertainty_score":0.999986,"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."}}