{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001289087,0.000166412,0.0001787855,0.00008514515,0.00005945366,0.0002224424,0.001461704,0.0002632871,0.00004948757],"category_scores_gemma":[0.000004818537,0.0001546,0.0001385297,0.0001084883,0.00001604879,0.0001151575,0.002081341,0.0003602628,0.000210906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006425237,"about_ca_system_score_gemma":0.0001765305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005779273,"about_ca_topic_score_gemma":0.000002186073,"domain_scores_codex":[0.9988264,0.00001690374,0.0002123823,0.0005756761,0.0002026359,0.0001659647],"domain_scores_gemma":[0.9983092,0.00001962071,0.0001110193,0.001419715,0.00008055339,0.00005992108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005133734,0.00008899592,0.00003886043,0.00006295185,0.00003948908,0.000001793589,0.0001340728,0.003555615,0.0003391234,0.709723,0.114192,0.1718189],"study_design_scores_gemma":[0.00006203646,0.00001607596,0.00002998524,0.00001084658,0.000005513202,0.000002444272,0.00000147315,0.9060661,0.001443798,0.07849641,0.01359954,0.0002658427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000300665,0.00001870374,0.9337087,0.0008536802,0.0009209861,0.0003831256,0.000006295305,0.001110845,0.06269701],"genre_scores_gemma":[0.2610932,0.00003819587,0.7189392,0.0003308461,0.0004010472,0.0002100813,0.000008969833,0.00001710273,0.01896132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9025104,"threshold_uncertainty_score":0.6304405,"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."}}