{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003214091,0.0001366699,0.0001584566,0.00008915845,0.001060803,0.003539922,0.00113552,0.00008370892,0.000001653441],"category_scores_gemma":[0.00007809707,0.0001208691,0.00004921446,0.00006592106,0.00003511521,0.01189939,0.0002169793,0.0001081584,0.00002359842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000283946,"about_ca_system_score_gemma":0.00004013951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004680417,"about_ca_topic_score_gemma":0.000001967796,"domain_scores_codex":[0.9988604,0.00001929654,0.0004230576,0.0001742397,0.000255012,0.0002679941],"domain_scores_gemma":[0.9984395,0.00003842583,0.0005269656,0.0006868832,0.0002253345,0.00008289015],"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":[0.00003876837,0.00002059625,0.0002082496,0.001026853,0.00001557277,0.000003937173,0.003318322,0.04522686,0.0001816282,0.0163427,0.03766422,0.8959523],"study_design_scores_gemma":[0.000300913,0.00001941351,0.00008893108,0.0001241363,0.000003008269,0.00002555861,0.00007616563,0.984089,0.00007065778,0.0003857506,0.01465959,0.0001568655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005483937,0.00007349964,0.9857671,0.0004686409,0.002262212,0.0003665607,0.00002136983,0.0003921009,0.005164569],"genre_scores_gemma":[0.9663628,0.000006621132,0.0326747,0.0003031146,0.0002311473,0.00007044137,0.00003513385,0.000008812941,0.0003072042],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9608789,"threshold_uncertainty_score":0.9974945,"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."}}