{"id":"W3116578073","doi":"10.1111/coin.12429","title":"Mixture‐based clustering for count data using approximated Fisher Scoring and Minorization–Maximization approaches","year":2020,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Mixture model; Count data; Dirichlet distribution; Multinomial distribution; Computer science; Hyperparameter; Burstiness; Overdispersion; Mathematics; Algorithm; Artificial intelligence; Statistics; Poisson distribution","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003246255,0.0001670044,0.0001743939,0.00006741295,0.000185663,0.000285861,0.0007343309,0.00006544715,0.000003883537],"category_scores_gemma":[0.0001591883,0.0001702176,0.00002877641,0.0003813434,0.00005193839,0.0006611402,0.0003493717,0.0000901656,0.000001507452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002777612,"about_ca_system_score_gemma":0.0001107769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000052089,"about_ca_topic_score_gemma":0.000001219682,"domain_scores_codex":[0.9985443,0.00006688126,0.0003089843,0.0006651392,0.0002217641,0.0001929331],"domain_scores_gemma":[0.9990053,0.0002594546,0.0001330499,0.0003177196,0.000168312,0.0001161203],"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":[0.00001961369,0.0000302707,0.0001326922,0.0001705157,0.00001933505,0.000001815666,0.0005169917,0.8833002,0.0001391897,0.04551825,0.00009822508,0.0700529],"study_design_scores_gemma":[0.0001204193,0.00003049586,0.00005306705,0.00003483498,0.00001285397,0.000007083495,0.00001473137,0.9843262,0.0005333557,0.01451459,0.0001579638,0.0001943685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002969166,0.000189533,0.9978302,0.0009429141,0.0001542864,0.000386208,0.00002630217,0.0001110382,0.00006258774],"genre_scores_gemma":[0.175479,0.000003586776,0.8235506,0.0007093915,0.00009850167,0.00001175764,0.0001281959,0.00001422039,0.000004818562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1751821,"threshold_uncertainty_score":0.6941273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.348640572375999,"score_gpt":0.340463679043445,"score_spread":0.008176893332554003,"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."}}