{"id":"W2165091599","doi":"10.48550/arxiv.1207.1375","title":"Nonparametric Bayesian Logic","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Syntax; Inference; Bayesian inference; Artificial intelligence; Dirichlet distribution; Matching (statistics); Dirichlet process; Generative model; Bayesian probability; Generative grammar; Machine learning; Latent Dirichlet allocation; Selection (genetic algorithm); Theoretical computer science; Data mining; Topic model; Mathematics","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":[0.01239364,0.001232795,0.001509672,0.003676898,0.00232548,0.006278349,0.00291896,0.002617173,0.007175275],"category_scores_gemma":[0.02075109,0.0009009123,0.002116327,0.004289648,0.005932827,0.008947446,0.00319517,0.004953393,0.001828471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00463045,"about_ca_system_score_gemma":0.003311543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004619779,"about_ca_topic_score_gemma":0.003489665,"domain_scores_codex":[0.9917356,0.004393526,0.0006337928,0.001108562,0.001836585,0.000291786],"domain_scores_gemma":[0.9891632,0.007778978,0.0005744924,0.0009321265,0.001260073,0.0002912015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004928634,0.000005251495,0.00007145722,0.0000367081,0.00001024259,0.00002483785,0.00007249777,0.001401942,0.00004076619,0.9893304,0.001552575,0.007448418],"study_design_scores_gemma":[0.000005222387,0.000002327993,0.00003306889,0.00002218745,0.000004949921,0.00002738664,0.00001453387,0.007662425,0.00004644044,0.9834172,0.008757669,0.000006482405],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001978071,0.001946802,0.9604924,0.005227639,0.0001949908,0.0000972554,0.001115295,0.0003582786,0.02858922],"genre_scores_gemma":[0.2051454,0.005055832,0.7675223,0.004172502,0.001462293,0.0008837194,0.002032604,0.0002956779,0.01342964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01239364,"threshold_uncertainty_score":0.06554461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07321097545028427,"score_gpt":0.203399019088266,"score_spread":0.1301880436379817,"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."}}