{"id":"W2755882125","doi":"10.2174/1876527001708010027","title":"Bayesian Inference for Three Bivariate Beta Binomial Models","year":2017,"lang":"en","type":"article","venue":"The Open Statistics & Probability Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Markov chain Monte Carlo; Beta-binomial distribution; Bayesian probability; Bayesian inference; Mathematics; Negative binomial distribution; Likelihood function; Statistics; Inference; Econometrics; Computer science; Maximum likelihood; Poisson distribution; 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":["metaresearch","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005285195,0.0003352485,0.0006744455,0.00004149981,0.00276736,0.002938801,0.003461394,0.0001268038,0.0003770053],"category_scores_gemma":[0.01013976,0.0002163936,0.000125519,0.00005922789,0.0006470905,0.0006480003,0.0008951146,0.0006583005,0.00001235869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001172308,"about_ca_system_score_gemma":0.0005282375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002362779,"about_ca_topic_score_gemma":0.0006251145,"domain_scores_codex":[0.9970936,0.0004131411,0.0009602003,0.0004454806,0.0004520349,0.0006355152],"domain_scores_gemma":[0.9913989,0.005005683,0.001019716,0.001605162,0.0006752391,0.0002952764],"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":[0.0002082563,0.0001320521,0.000439565,0.00007946164,0.00007700744,0.000008698429,0.0002419064,0.00001985406,0.00001790363,0.9027768,0.00191839,0.09408014],"study_design_scores_gemma":[0.001000544,0.0002061582,0.00245347,0.00009379749,0.0001728874,0.00004111517,0.00001699222,0.05089642,0.00002909283,0.9445104,0.0002773569,0.000301785],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005878289,0.00001192978,0.9905903,0.001588452,0.0005103039,0.001761893,0.000939947,0.0000223826,0.003986926],"genre_scores_gemma":[0.09511543,0.0000135857,0.9042314,0.00007719103,0.000273229,0.0001116377,0.000005986871,0.00004044941,0.0001310895],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0945276,"threshold_uncertainty_score":0.9985309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2415924702581883,"score_gpt":0.4466584665223155,"score_spread":0.2050659962641272,"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."}}