{"id":"W2786105260","doi":"10.1109/ascc.2017.8287319","title":"Simultaneous estimation of sub-model number and parameters for mixture probability principal component regression","year":2017,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Principal component analysis; Probabilistic logic; Maximum a posteriori estimation; Computer science; Expectation–maximization algorithm; Statistical model; A priori and a posteriori; Principal component regression; Maximization; Component (thermodynamics); Algorithm; Artificial intelligence; Data mining; Mathematical optimization; Mathematics; Statistics; Maximum likelihood","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":[0.0004976332,0.000129156,0.0002182895,0.00001844289,0.0002156517,0.0001181544,0.0004517256,0.00009486245,9.95562e-7],"category_scores_gemma":[0.0003148329,0.00009112293,0.0000613495,0.0000233595,0.0001035381,0.0003095283,0.0002114476,0.00007425608,6.821386e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001801606,"about_ca_system_score_gemma":0.00003596759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002040378,"about_ca_topic_score_gemma":0.000007837169,"domain_scores_codex":[0.9990449,0.00005049771,0.0002189566,0.0003618906,0.0001540813,0.0001696979],"domain_scores_gemma":[0.9985709,0.0002402637,0.0002126391,0.0007892116,0.0001026909,0.00008427724],"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":[0.00009863404,0.0001844761,0.0005829331,0.0002768231,0.00002362593,0.000003945841,0.0007405192,0.01418376,0.005867764,0.2478001,0.0001605555,0.7300768],"study_design_scores_gemma":[0.0002764832,0.00003698677,0.0002530886,0.00003472262,0.00000738945,0.000003440313,6.553727e-7,0.8344972,0.01204813,0.1527175,0.0000255606,0.00009881634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2034448,0.00001026765,0.7952536,0.0004144908,0.00009209381,0.0004005952,0.000004053276,0.00003377051,0.0003464057],"genre_scores_gemma":[0.4826291,0.000002597995,0.5172626,0.00002944029,0.000005079278,0.00001124883,0.000001017802,0.000003247932,0.00005561366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8203135,"threshold_uncertainty_score":0.3715885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03418111093335344,"score_gpt":0.3202447601942225,"score_spread":0.286063649260869,"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."}}