{"id":"W2296528819","doi":"10.1109/icmla.2015.70","title":"Topic Novelty Detection Using Infinite Variational Inverted Dirichlet Mixture Models","year":2015,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Dirichlet distribution; Dirichlet process; Hierarchical Dirichlet process; Conjugate prior; Inference; Prior probability; Mixture model; Bayesian inference; Novelty; Bayes' theorem; Latent Dirichlet allocation; Computer science; Novelty detection; Mathematics; Artificial intelligence; Parametric model; Bayes factor; Parametric statistics; Bayesian probability; Pattern recognition (psychology); Topic model; Statistics; Boundary value problem; Mathematical analysis","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":[0.0004523881,0.0001365389,0.0001448711,0.0001154502,0.00008493102,0.0001287449,0.0003599219,0.0001287374,0.00001052717],"category_scores_gemma":[0.00005259781,0.0001153123,0.00005187004,0.0004581567,0.00001691922,0.0008695934,0.0001572742,0.0001515208,0.00001144106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007727116,"about_ca_system_score_gemma":0.0001476185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001369244,"about_ca_topic_score_gemma":0.00002618866,"domain_scores_codex":[0.9988332,0.0001206686,0.0002021102,0.0003241491,0.0003044287,0.0002154171],"domain_scores_gemma":[0.9991395,0.00004994322,0.00007154309,0.0003697407,0.0002076344,0.0001616753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001775323,0.0001132082,0.0002299754,0.00001419373,0.00005259046,0.000008180557,0.001734004,0.01364564,0.005198859,0.8587766,0.0009882255,0.1192207],"study_design_scores_gemma":[0.0002638567,0.00002172742,0.0001696413,0.000003721959,0.000005330405,0.00001948595,0.000003057358,0.7663777,0.0009204526,0.2314149,0.0006733636,0.000126802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004525641,0.00004277733,0.9872925,0.0004209163,0.0005571207,0.0001076182,0.000001516967,0.0001605895,0.006891271],"genre_scores_gemma":[0.324139,0.000001380822,0.674494,0.0008804816,0.0001191157,0.000004066025,0.000001756679,0.000006343934,0.0003538967],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.752732,"threshold_uncertainty_score":0.4702298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07779213641419455,"score_gpt":0.2859294368449303,"score_spread":0.2081373004307358,"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."}}