{"id":"W2593882186","doi":"10.5539/ijsp.v6n2p134","title":"On Some Mixture Models for Over-dispersed Binary Data","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Applied mathematics; Binary number; Binomial (polynomial); Covariate; Quasi-likelihood; Mixture model; Computation; Binary data; Range (aeronautics); Count data; Statistics; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02881501,0.00225552,0.003436296,0.004425627,0.001715133,0.004130203,0.005064708,0.003514797,0.007139803],"category_scores_gemma":[0.06656341,0.001761746,0.004985464,0.005613754,0.003277154,0.006771851,0.003850142,0.005483564,0.001949831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003089571,"about_ca_system_score_gemma":0.00185621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01260168,"about_ca_topic_score_gemma":0.01053748,"domain_scores_codex":[0.9874466,0.007983761,0.0004746555,0.002202083,0.00134758,0.0005452997],"domain_scores_gemma":[0.9598628,0.03336831,0.002312643,0.0017867,0.002224904,0.0004446503],"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":[0.0002318559,0.0001021995,0.004418789,0.000470683,0.0003499634,0.000458916,0.00130367,0.2483706,0.0007079772,0.6750643,0.003444817,0.06507619],"study_design_scores_gemma":[0.00004718495,0.00005528387,0.001169698,0.0001881347,0.0001138051,0.0002053561,0.0001439791,0.6974283,0.0002514779,0.2944232,0.005898144,0.00007547191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007093713,0.001021155,0.9895169,0.0006281452,0.00007168913,0.00009784978,0.0002240321,0.000150884,0.001195572],"genre_scores_gemma":[0.1963549,0.004512839,0.7815354,0.001010479,0.0005939976,0.001380446,0.002227065,0.0003934732,0.01199142],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02881501,"threshold_uncertainty_score":0.1523902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06257641469814794,"score_gpt":0.3540193746940929,"score_spread":0.2914429599959449,"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."}}