{"id":"W2154036191","doi":"10.48550/arxiv.math/0703292","title":"Nonlinear Models Using Dirichlet Process Mixtures","year":2007,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":224,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dirichlet distribution; Multinomial logistic regression; Component (thermodynamics); Nonlinear system; Computer science; Support vector machine; Dirichlet process; Class (philosophy); Mixture model; Covariate; Machine learning; Multinomial distribution; Artificial intelligence; Process (computing); Mathematics; Pattern recognition (psychology); Econometrics","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":[0.0008351275,0.0002220071,0.0002382049,0.0001305982,0.0001786628,0.00008195476,0.000871208,0.0001383578,0.000007416011],"category_scores_gemma":[0.00004416251,0.0001901636,0.00009410369,0.0005564294,0.00005573503,0.0007275113,0.0001848763,0.0002530669,0.0000198674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003622836,"about_ca_system_score_gemma":0.00009036568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003016156,"about_ca_topic_score_gemma":0.000006700303,"domain_scores_codex":[0.9982269,0.00006265631,0.000315254,0.000543033,0.0003022865,0.0005498575],"domain_scores_gemma":[0.9988172,0.00008871638,0.000115144,0.0006414012,0.000147461,0.0001900716],"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":[0.0002328462,0.002123885,0.1478432,0.0006091644,0.000432732,0.001396099,0.02011484,0.008360011,0.1753777,0.2222236,0.002410217,0.4188758],"study_design_scores_gemma":[0.0008295099,0.0001303672,0.005053224,0.0001081261,0.00004369495,0.0001326448,0.00004674362,0.7785844,0.1137467,0.09846864,0.001768964,0.001087029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3437844,0.0002535867,0.6535715,0.0001771195,0.0002469791,0.0001032174,0.000001214707,0.0001329247,0.001728998],"genre_scores_gemma":[0.5600075,0.000008063153,0.4387996,0.0008046918,0.0002219374,0.000002502964,0.000001131714,0.00001652836,0.0001381117],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7702243,"threshold_uncertainty_score":0.7754645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06584906040963906,"score_gpt":0.3317421120016123,"score_spread":0.2658930515919732,"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."}}