{"id":"W4396688170","doi":"10.1080/10618600.2024.2350476","title":"Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Dalhousie University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Canada Foundation for Innovation","keywords":"Hidden Markov model; Inference; Algorithm; Data set; Computer science; Set (abstract data type); Computation; Variance (accounting); Expectation–maximization algorithm; Statistical inference; Mathematical optimization; Mathematics; Artificial intelligence; Statistics; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004292669,0.001430889,0.001950422,0.001322259,0.0007752823,0.001115105,0.002343723,0.001556072,0.004159219],"category_scores_gemma":[0.02112848,0.001596896,0.001776266,0.0014504,0.001227324,0.001463035,0.002171925,0.003576946,0.001477987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001560354,"about_ca_system_score_gemma":0.003359127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01455451,"about_ca_topic_score_gemma":0.01743797,"domain_scores_codex":[0.9976291,0.001353699,0.0001594866,0.0004122846,0.0003232789,0.000122247],"domain_scores_gemma":[0.9912161,0.007524481,0.0003097256,0.0004266715,0.0004120145,0.0001110543],"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":[0.00006708265,0.00005553016,0.0007086871,0.0001120914,0.00009427441,0.00006985025,0.00008483034,0.9064433,0.001057465,0.02578977,0.001985001,0.06353207],"study_design_scores_gemma":[0.000006127415,0.000006002858,0.00004090173,0.000005497925,0.000003121976,0.000005592777,0.000003294732,0.9919356,0.0001745246,0.007548139,0.000267076,0.000004129609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001489374,0.0001081078,0.997426,0.00007880887,0.00001201755,0.00002449571,0.00004555595,0.0005493394,0.0002662779],"genre_scores_gemma":[0.09288128,0.0002424303,0.9036992,0.0001832373,0.0000689448,0.0004413938,0.0006446382,0.0006365352,0.00120227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01455451,"threshold_uncertainty_score":0.02893955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791005297167532,"score_gpt":0.2679284535250385,"score_spread":0.2500184005533632,"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."}}