{"id":"W2162721377","doi":"10.1109/icassp.2007.366870","title":"Bayesian Unsupervised Signal Classification by Dirichlet Process Mixtures of Gaussian Processes","year":2007,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dirichlet process; Cluster analysis; Mixture model; Dirichlet distribution; Pattern recognition (psychology); Hierarchical Dirichlet process; Computer science; A priori and a posteriori; Gibbs sampling; Bayesian probability; Markov chain Monte Carlo; Gaussian process; Algorithm; Artificial intelligence; Gaussian; Mathematics; Latent Dirichlet allocation; Topic model; Physics","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":[0.004791568,0.0009606682,0.002194698,0.002687746,0.00109,0.002587163,0.002789531,0.002403913,0.001843622],"category_scores_gemma":[0.01671843,0.001010061,0.001896612,0.002151596,0.002383584,0.003126453,0.002483043,0.002841451,0.001153683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001428144,"about_ca_system_score_gemma":0.0010558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002640906,"about_ca_topic_score_gemma":0.002037821,"domain_scores_codex":[0.9954454,0.002250595,0.0002064868,0.0008194894,0.0009667182,0.0003113041],"domain_scores_gemma":[0.9939623,0.0045573,0.0004181872,0.0005000089,0.0004319251,0.0001303323],"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":[0.0004711189,0.0001427883,0.001567019,0.0002586463,0.0001730192,0.0001390999,0.0005385319,0.5202634,0.005623476,0.2237846,0.00387382,0.2431645],"study_design_scores_gemma":[0.00001259245,0.0000138266,0.0002009402,0.00001607419,0.00001042938,0.0000347044,0.00001530417,0.9301314,0.0007484389,0.06788243,0.0009088206,0.00002501744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004437427,0.0002270543,0.9946316,0.0001084045,0.00002170235,0.00002051448,0.00002932131,0.0001594399,0.0003644529],"genre_scores_gemma":[0.2589738,0.001087901,0.7337921,0.0002715934,0.0003695423,0.0004114296,0.0007009739,0.0002777159,0.004115097],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004791568,"threshold_uncertainty_score":0.02534056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779959529025576,"score_gpt":0.2894059295871527,"score_spread":0.2716063342968969,"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."}}