{"id":"W4300821755","doi":"10.1007/978-3-030-99142-5_6","title":"Bayesian Inference of Hidden Markov Models Using Dirichlet Mixtures","year":2012,"lang":"en","type":"book-chapter","venue":"Unsupervised and semi-supervised learning","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Markov chain Monte Carlo; Hierarchical Dirichlet process; Hidden Markov model; Computer science; Dirichlet process; Variable-order Bayesian network; Reversible-jump Markov chain Monte Carlo; Artificial intelligence; Bayesian inference; Machine learning; Inference; Dirichlet distribution; Model selection; Benchmark (surveying); Bayesian probability; Gibbs sampling; Latent Dirichlet allocation; Topic model; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00110679,0.001144506,0.001590114,0.0006704863,0.0004283947,0.0002821408,0.001488399,0.001102824,0.0003117847],"category_scores_gemma":[0.00009851687,0.001082647,0.0004643346,0.0002395063,0.000262873,0.001148906,0.0009989316,0.001734279,0.00001103603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009043054,"about_ca_system_score_gemma":0.0002923644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008417397,"about_ca_topic_score_gemma":0.000004537567,"domain_scores_codex":[0.9952137,0.0003779845,0.001130029,0.001377131,0.0008853261,0.001015769],"domain_scores_gemma":[0.9966018,0.0005674187,0.0005432161,0.001321478,0.0003545855,0.0006115604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006092608,0.00008949432,0.0003263231,0.0008632275,0.0003743423,0.00006755057,0.006482844,0.000612204,0.00625066,0.2974263,0.0001889985,0.6872571],"study_design_scores_gemma":[0.001509638,0.000291014,0.00004245179,0.00179256,0.0004379009,0.0001330875,0.0000751243,0.8180363,0.0007375558,0.1645259,0.00976184,0.002656615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006941023,0.01476226,0.8926979,0.0001267919,0.0003221201,0.000485677,0.00002734998,0.0002634963,0.09062031],"genre_scores_gemma":[0.2400931,0.006526901,0.7050632,0.0007123747,0.000893437,0.00002749738,0.0001071457,0.0003850849,0.04619128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8174241,"threshold_uncertainty_score":0.9991624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312889631722289,"score_gpt":0.2585312073238452,"score_spread":0.2254023110066223,"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."}}