{"id":"W3008218724","doi":"10.5539/ijsp.v9n2p38","title":"Bayesian Estimation of Parameters of Weibull Distribution Using Linex Error Loss Function","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Mathematics; Mean squared error; Bayes estimator; Scale parameter; Weibull distribution; Bayes' theorem; Shape parameter; Function (biology); Bayesian probability; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003709749,0.00009919994,0.0002591001,0.00004115669,0.00003449422,0.00002159682,0.0001339075,0.00005196586,0.00009974155],"category_scores_gemma":[0.002806591,0.00009096766,0.00006326861,0.0001239924,0.0001869527,0.0001211955,0.00003261106,0.0001216789,7.464313e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006457616,"about_ca_system_score_gemma":0.00009259406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001391644,"about_ca_topic_score_gemma":0.000001589418,"domain_scores_codex":[0.9983535,0.00006832941,0.0009454887,0.0001182596,0.0004354884,0.00007890776],"domain_scores_gemma":[0.9971481,0.0005650177,0.0009215846,0.00008264773,0.001174523,0.000108103],"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":[0.0005408628,0.0004870581,0.002135634,0.000420133,0.0002105321,0.000005147215,0.0003248336,0.006938003,0.001084151,0.9652324,0.0009250285,0.02169622],"study_design_scores_gemma":[0.000759904,0.0002993131,0.01013122,0.00009736075,0.0001699042,0.00002595581,0.00008001788,0.3970915,0.001304222,0.5898284,0.00009863852,0.000113533],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1304959,0.0000135361,0.866154,0.0006828432,0.0001009823,0.0001323673,0.002392907,0.0000062923,0.00002115507],"genre_scores_gemma":[0.768898,0.000008617591,0.2309096,0.0000314928,0.00002869981,0.000001699177,0.0001160108,0.000004880246,0.000001029782],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.638402,"threshold_uncertainty_score":0.3709554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07804999641280497,"score_gpt":0.3617715317365339,"score_spread":0.283721535323729,"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."}}