{"id":"W3152404744","doi":"10.1002/cjs.11671","title":"Bayesian clustering for continuous‐time hidden Markov models","year":2021,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Markov chain Monte Carlo; Gibbs sampling; Cluster analysis; Computer science; Dirichlet process; Merge (version control); Algorithm; Hierarchical Dirichlet process; Bayesian inference; Bayesian probability; Mathematics; Artificial intelligence; Latent Dirichlet allocation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005627922,0.001111274,0.001393923,0.002557438,0.0013192,0.001857955,0.003970174,0.002031477,0.004309353],"category_scores_gemma":[0.0221733,0.001150799,0.001977521,0.00245482,0.002168059,0.002813707,0.002199227,0.003493723,0.001652066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003221176,"about_ca_system_score_gemma":0.003632142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01405606,"about_ca_topic_score_gemma":0.01289243,"domain_scores_codex":[0.9970803,0.001379296,0.0001328504,0.0006600291,0.0005950017,0.0001524216],"domain_scores_gemma":[0.9903203,0.006660541,0.0008027673,0.0009697943,0.00103527,0.0002113559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004901399,0.00004878511,0.001194614,0.0001555042,0.0001181423,0.00008330448,0.0002476823,0.499236,0.0009163607,0.4397089,0.003028657,0.05521303],"study_design_scores_gemma":[0.000008862025,0.00000643419,0.0001852345,0.00001950521,0.00001113711,0.00002622753,0.00001322684,0.8860781,0.0003056361,0.111664,0.001659619,0.00002199206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009519662,0.0001183095,0.9983189,0.0000859507,0.00001533277,0.00001777333,0.00004824553,0.0001265394,0.0003170427],"genre_scores_gemma":[0.1059769,0.0007572615,0.8881115,0.0001918978,0.0001454393,0.0004397089,0.0007914827,0.0002695678,0.00331614],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01405606,"threshold_uncertainty_score":0.02976364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475658175973065,"score_gpt":0.2522323345800609,"score_spread":0.2274757528203302,"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."}}