{"id":"W2079563949","doi":"10.1109/tsp.2005.850344","title":"Offline and online identification of hidden semi-Markov models","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hidden Markov model; Identification (biology); Computer science; Maximum-entropy Markov model; Markov chain; Markov model; Algorithm; Hidden semi-Markov model; Constant (computer programming); Markov process; Online algorithm; Forward algorithm; Estimation theory; Variable-order Markov model; Data mining; Artificial intelligence; Machine learning; Mathematics; 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":[0.0002230608,0.0001340325,0.0001606881,0.0001584301,0.0002117189,0.0001168588,0.0003128183,0.00008155911,0.00001635456],"category_scores_gemma":[0.000002003522,0.0001283909,0.00004661831,0.0003500391,0.00006432584,0.0009910191,0.00000393501,0.0002197044,0.000005384198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002254598,"about_ca_system_score_gemma":0.00004632238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007899586,"about_ca_topic_score_gemma":0.000009018172,"domain_scores_codex":[0.998717,0.00004036119,0.0004253162,0.0003537404,0.0002870506,0.000176519],"domain_scores_gemma":[0.9992723,0.00008561021,0.0001603405,0.0002688378,0.0001359039,0.00007702882],"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":[0.0000197117,0.0002033469,0.000002499202,0.00003643676,0.000009169416,0.000001122718,0.0004219976,0.1385022,0.01286182,0.0001164925,0.00006507766,0.8477601],"study_design_scores_gemma":[0.0002521298,0.00003793666,0.00003562453,0.00009518419,0.00001707371,0.00001781742,0.00003476132,0.9580823,0.04039466,0.0007071035,0.0001845044,0.000140891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01981813,0.0003071776,0.9789929,0.0004177504,0.0001026893,0.00008191445,0.00003009794,0.000142659,0.0001066727],"genre_scores_gemma":[0.9428437,0.00007764479,0.0566746,0.0001342191,0.00007610769,0.000005155504,0.000005612093,0.00001195528,0.0001710379],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9230255,"threshold_uncertainty_score":0.523563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343747570696979,"score_gpt":0.2598130046594229,"score_spread":0.2363755289524531,"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."}}