{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005831095,0.001117273,0.002346632,0.003176855,0.001104351,0.003297184,0.003941908,0.002223375,0.001912031],"category_scores_gemma":[0.02014579,0.001139168,0.002800385,0.002461728,0.001548315,0.004509086,0.003348034,0.003519063,0.001032118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314097,"about_ca_system_score_gemma":0.00120846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004219281,"about_ca_topic_score_gemma":0.003916551,"domain_scores_codex":[0.9961982,0.001632648,0.0001708542,0.0008601284,0.0009067282,0.0002313673],"domain_scores_gemma":[0.9914751,0.006465703,0.0005224838,0.0006975867,0.000635332,0.000203829],"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":[0.0006149865,0.0003185793,0.008034346,0.000409735,0.0006520674,0.0005087649,0.001198152,0.423231,0.01535259,0.1638686,0.006362883,0.3794484],"study_design_scores_gemma":[0.00001284923,0.00001562358,0.0002983864,0.00001107795,0.00001900624,0.00007185682,0.00002029044,0.9538094,0.001188232,0.04376113,0.000766059,0.0000259862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005457294,0.0001754853,0.9935336,0.0001212707,0.00002800183,0.00002635123,0.00005505267,0.0002262526,0.0003766405],"genre_scores_gemma":[0.3898969,0.000606901,0.6042379,0.0002897582,0.0002946619,0.0003032095,0.001064575,0.0003355946,0.002970606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005831095,"threshold_uncertainty_score":0.03083813,"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."}}