{"id":"W2149940508","doi":"10.1109/tit.2007.909168","title":"Information conversion, effective samples, and parameter size","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute","keywords":"Principle of maximum entropy; Kullback–Leibler divergence; Sample size determination; Mathematics; Entropy (arrow of time); Bayesian probability; Independence (probability theory); Posterior probability; Sample (material); Statistics; Computer science","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":[0.01083976,0.0009645141,0.001218152,0.002868064,0.001034278,0.003457492,0.001985435,0.002065193,0.003970255],"category_scores_gemma":[0.08889478,0.0009034412,0.0008503724,0.001874017,0.005304604,0.009216029,0.003676599,0.002489883,0.0004621676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002027705,"about_ca_system_score_gemma":0.001123335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007298169,"about_ca_topic_score_gemma":0.000740451,"domain_scores_codex":[0.9939242,0.002990637,0.000276301,0.0009489155,0.001568286,0.0002916762],"domain_scores_gemma":[0.9537235,0.03915399,0.001378233,0.003655223,0.00160137,0.0004877443],"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":[0.0001438668,0.00005954302,0.001644254,0.0001649292,0.00007022435,0.0001528883,0.0002711906,0.09524222,0.002648553,0.8380401,0.001093298,0.06046902],"study_design_scores_gemma":[0.00002979894,0.00005126609,0.0007472509,0.00006330734,0.00003363254,0.0002164852,0.00006411838,0.2090577,0.003340633,0.7842849,0.002060555,0.00005042289],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02338144,0.000751466,0.9672875,0.0008508338,0.00006591697,0.00007285483,0.0001063977,0.000170534,0.007313092],"genre_scores_gemma":[0.6624388,0.0008801933,0.3315512,0.0004792246,0.0002341893,0.0006453978,0.0002581934,0.000346283,0.003166474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01083976,"threshold_uncertainty_score":0.05732679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007705106554193773,"score_gpt":0.2319841043563846,"score_spread":0.2242789978021908,"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."}}