{"id":"W2954489650","doi":"","title":"Bayesian Nonparametric Clustering of Continuous-Time Hidden Markov Models for Health Trajectories","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dirichlet process; Cluster analysis; Markov chain Monte Carlo; Hierarchical Dirichlet process; Gibbs sampling; Inference; Computer science; Markov chain; Bayesian inference; Markov model; Hidden Markov model; Nonparametric statistics; Bayesian probability; Econometrics; Data mining; Mathematics; Artificial intelligence; Machine learning; Topic model; Latent Dirichlet allocation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008609835,0.001161787,0.001752391,0.003328225,0.001453071,0.002231372,0.004346394,0.001998366,0.00346543],"category_scores_gemma":[0.03888974,0.001285221,0.002509184,0.002992132,0.002221481,0.002777415,0.002588544,0.00339186,0.001156841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003740485,"about_ca_system_score_gemma":0.003897787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03547591,"about_ca_topic_score_gemma":0.03601137,"domain_scores_codex":[0.9956896,0.002381352,0.0001986459,0.0009132172,0.000562066,0.0002550781],"domain_scores_gemma":[0.9805336,0.01573507,0.001156827,0.001202941,0.001069927,0.0003017049],"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":[0.00007640301,0.00006916116,0.003036393,0.0001096504,0.0001879914,0.00008437812,0.0003366786,0.7777557,0.0004313842,0.1703958,0.002351809,0.04516466],"study_design_scores_gemma":[0.000006437993,0.000006516964,0.0003853094,0.0000180277,0.0000112404,0.00001641759,0.00002055546,0.9286698,0.0001260697,0.07005725,0.0006661001,0.00001643357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003247428,0.0001132335,0.9958554,0.0001685434,0.00001554449,0.00003801842,0.0001455992,0.0001401011,0.0002761257],"genre_scores_gemma":[0.3018491,0.0008224152,0.6886676,0.000250899,0.0001983138,0.000769329,0.002564758,0.0003021607,0.004575527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03547591,"threshold_uncertainty_score":0.07053882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05077042648064682,"score_gpt":0.2234998433638747,"score_spread":0.1727294168832279,"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."}}