{"id":"W4386608500","doi":"10.1080/10618600.2023.2257258","title":"Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR)","year":2023,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Expectation–maximization algorithm; Cluster analysis; Computer science; Covariate; Context (archaeology); Maximization; Simplex; Markov chain; Mixture model; Mathematical optimization; Data mining; Artificial intelligence; Machine learning; Mathematics; Statistics; Maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004240816,0.001382242,0.002002417,0.00176791,0.0009018552,0.001427593,0.002042726,0.001433311,0.003203929],"category_scores_gemma":[0.01248815,0.001206685,0.00228578,0.002047168,0.0008365497,0.001559319,0.002235596,0.002900814,0.001291438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158912,"about_ca_system_score_gemma":0.003849596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009126483,"about_ca_topic_score_gemma":0.01082752,"domain_scores_codex":[0.9980879,0.0009890478,0.0001280646,0.0004328986,0.0002732848,0.0000887859],"domain_scores_gemma":[0.9960337,0.002623837,0.0003795808,0.0003985145,0.0004380701,0.0001262767],"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":[0.0002405691,0.00009588297,0.002810937,0.0002548007,0.0002727893,0.0001596334,0.0002513144,0.8171959,0.003072722,0.03800366,0.003937387,0.1337043],"study_design_scores_gemma":[0.00001161425,0.00001275201,0.0001354854,0.00000943322,0.0000083337,0.00002494075,0.000009189347,0.9883373,0.0005375664,0.01015478,0.0007456889,0.00001280882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002153037,0.00005623767,0.9968628,0.00004920527,0.000009198459,0.00005890015,0.00009498468,0.000590242,0.0001254069],"genre_scores_gemma":[0.04948292,0.0001299379,0.9480897,0.00006726771,0.00002919707,0.0004725383,0.0008124681,0.0002639251,0.0006520618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009126483,"threshold_uncertainty_score":0.0224278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04511387327722369,"score_gpt":0.3033294974391578,"score_spread":0.2582156241619341,"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."}}