{"id":"W2044853137","doi":"10.5539/ijsp.v4n2p10","title":"A Bayesian Mixture Model Accounting for Zeros and Negatives in the Loss Triangle","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Markov chain Monte Carlo; Bayesian probability; Multinomial distribution; Poisson distribution; Log-normal distribution; Statistics; Markov chain; Applied mathematics; Econometrics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.008020526,0.00008794649,0.0002289855,0.00010445,0.00006209025,0.0003205653,0.0004899086,0.00005141441,0.000004582936],"category_scores_gemma":[0.008335463,0.00004900953,0.00005013764,0.00009871974,0.0002081539,0.0003795644,0.00007322327,0.0001775594,3.347565e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004451116,"about_ca_system_score_gemma":0.000196903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002161547,"about_ca_topic_score_gemma":0.00009626931,"domain_scores_codex":[0.9980248,0.0001999392,0.0007202005,0.0001865043,0.0007569926,0.0001115888],"domain_scores_gemma":[0.9957499,0.002265374,0.000398649,0.0001262129,0.001379391,0.00008050622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003505623,0.001042841,0.1512274,0.00009203891,0.0001834989,0.000062037,0.04730329,0.03397024,0.0001485055,0.4685682,0.01033424,0.283562],"study_design_scores_gemma":[0.000831078,0.00009607413,0.004141415,0.00001580156,0.00000794169,0.00004214495,0.0004297236,0.1301852,0.00001122948,0.8635158,0.0006712794,0.00005231199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.323506,0.0002163105,0.6708902,0.004585855,0.000201424,0.0002337175,0.000265553,0.000001787197,0.00009922084],"genre_scores_gemma":[0.9314349,0.00003333909,0.06819313,0.000213872,0.00009031998,0.000005673838,0.000002083608,0.000003122547,0.00002357689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6079289,"threshold_uncertainty_score":0.9978929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1227314291660379,"score_gpt":0.3952751041206155,"score_spread":0.2725436749545775,"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."}}