{"id":"W3004816779","doi":"10.1002/cjs.11645","title":"A test for independence via Bayesian nonparametric estimation of mutual information","year":2021,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mutual information; Frequentist inference; Independence (probability theory); Dirichlet process; Nonparametric statistics; Conditional independence; Mathematics; Bayesian probability; Econometrics; Estimation; Statistics; Computer science; Artificial intelligence; Data mining; Bayesian inference; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01166769,0.0008668631,0.002007071,0.003593492,0.001138314,0.00267907,0.002405567,0.001908543,0.004144826],"category_scores_gemma":[0.0938765,0.00062437,0.001380168,0.002630042,0.003047483,0.00424339,0.003716195,0.00245119,0.001164327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007843366,"about_ca_system_score_gemma":0.001921256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001295869,"about_ca_topic_score_gemma":0.0007724348,"domain_scores_codex":[0.9819104,0.01018817,0.0007335584,0.002038551,0.004544427,0.0005848519],"domain_scores_gemma":[0.93185,0.05805576,0.003327642,0.003512503,0.002659586,0.0005944305],"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":[0.0008917616,0.0003606042,0.01911915,0.0005128059,0.0007656323,0.0005612905,0.0007954087,0.130791,0.007348615,0.5207738,0.004773001,0.313307],"study_design_scores_gemma":[0.00008925152,0.0002868995,0.009044162,0.0001204205,0.0001128901,0.0006747749,0.0001797354,0.5779893,0.004940953,0.4021059,0.004297876,0.0001579066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02813673,0.0002468893,0.9670448,0.0002739668,0.00003226885,0.00008190546,0.0002614372,0.0003032435,0.003618791],"genre_scores_gemma":[0.6305325,0.0004512857,0.3645141,0.0002348867,0.0002301559,0.000567948,0.001113309,0.0002345441,0.002121324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01166769,"threshold_uncertainty_score":0.06170535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646882914035225,"score_gpt":0.2594717174292251,"score_spread":0.2430028882888729,"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."}}