{"id":"W4256482217","doi":"10.31224/osf.io/w9v2b","title":"Hidden Markov Model: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Viterbi algorithm; Computer science; Forward algorithm; Graphical model; Markov chain; Variable-order Markov model; Maximum-entropy Markov model; Markov property; Markov model; Variable-order Bayesian network; Expectation–maximization algorithm; Belief propagation; Artificial intelligence; Markov random field; Hidden semi-Markov model; Algorithm; Bayesian probability; Machine learning; Mathematics; Maximum likelihood; Bayesian inference; Statistics","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":[0.001734121,0.002573056,0.001720771,0.002306651,0.0004596182,0.001874837,0.002331358,0.002716907,0.05353926],"category_scores_gemma":[0.005348875,0.00101961,0.001642165,0.003657013,0.0007842298,0.004463142,0.001406447,0.003896577,0.02718066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247095,"about_ca_system_score_gemma":0.00131521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002815219,"about_ca_topic_score_gemma":0.002081538,"domain_scores_codex":[0.9988112,0.0004121251,0.0001179676,0.0002822898,0.000309756,0.00006649583],"domain_scores_gemma":[0.9974912,0.001794191,0.0001205109,0.0001991786,0.0003212371,0.00007361058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000786095,0.0001206327,0.0008093276,0.00445814,0.0002567061,0.0005115923,0.0003380112,0.0248358,0.002003896,0.2373524,0.3411666,0.3880683],"study_design_scores_gemma":[0.0000126546,0.00005415293,0.000414342,0.0006524103,0.00005320538,0.0005510484,0.00003950658,0.02456653,0.0003791214,0.1615132,0.8117149,0.00004895992],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001091029,0.2270836,0.6988691,0.004364469,0.008509441,0.0002519222,0.005123405,0.004398392,0.05030861],"genre_scores_gemma":[0.03224713,0.3556525,0.4797077,0.007458847,0.02181799,0.001781082,0.01915331,0.003955143,0.07822639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05353926,"threshold_uncertainty_score":0.1791067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177542283244411,"score_gpt":0.2657312844134803,"score_spread":0.2479770560890392,"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."}}