{"id":"W2750673042","doi":"","title":"Inverse Filtering for Hidden Markov Models","year":2017,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hidden Markov model; Sequence (biology); Viterbi algorithm; Computer science; Filter (signal processing); Algorithm; Inverse; Markov chain; Simple (philosophy); Integer (computer science); Inverse problem; Markov model; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Computer vision","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":[0.002348222,0.0008326593,0.001046957,0.0006816081,0.0006255661,0.001531418,0.0009508836,0.001428431,0.002940983],"category_scores_gemma":[0.00951718,0.000612465,0.001163955,0.0006810423,0.001385222,0.00228988,0.001334024,0.002787455,0.0006037937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001368887,"about_ca_system_score_gemma":0.001145081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004768244,"about_ca_topic_score_gemma":0.003768316,"domain_scores_codex":[0.9989173,0.0004243874,0.00006437879,0.0002266471,0.0002870333,0.00008025529],"domain_scores_gemma":[0.9954194,0.003878204,0.0002141365,0.0001959941,0.0002349008,0.00005727726],"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":[0.00005749334,0.00004670223,0.0005991149,0.0001962094,0.00006923977,0.0001701715,0.0002251195,0.5073175,0.002346612,0.3987851,0.001077836,0.08910884],"study_design_scores_gemma":[0.000004508115,0.00001220613,0.00005170619,0.00001529828,0.000007918201,0.00002658668,0.00001193427,0.8969415,0.0005845731,0.1008235,0.001510406,0.000009830744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001410144,0.0001973409,0.9973918,0.0001033989,0.00002112774,0.000008369451,0.00001422774,0.00006733781,0.0007861944],"genre_scores_gemma":[0.308607,0.00200899,0.6788877,0.0003430313,0.000361346,0.0002697977,0.0003867418,0.0001944551,0.00894089],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004768244,"threshold_uncertainty_score":0.01241875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04701140439472471,"score_gpt":0.2737674705712582,"score_spread":0.2267560661765335,"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."}}