{"id":"W1536570457","doi":"10.1017/cbo9780511791277","title":"Bayesian logical data analysis for the physical sciences a comparative approach with Mathematica support","year":2005,"lang":"en","type":"book","venue":"","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":614,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Frequentist inference; Mathematics; Bayesian statistics; Statistical inference; Bayesian probability; Markov chain Monte Carlo; Bayesian inference; Principle of maximum entropy; Bayes factor; Fiducial inference; Statistics; Applied mathematics; Algorithm","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":[0.001679445,0.0003655594,0.001062803,0.0001681805,0.0003056228,0.0001567308,0.001441446,0.0001288112,0.0006669004],"category_scores_gemma":[0.0008212671,0.0001705022,0.0001922081,0.0003330074,0.0009718144,0.00007175883,0.0002253153,0.000263428,0.00002516431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005574289,"about_ca_system_score_gemma":0.0006921306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002227577,"about_ca_topic_score_gemma":0.00003260731,"domain_scores_codex":[0.9978545,0.0001422791,0.000405702,0.0007347838,0.0005463892,0.0003162905],"domain_scores_gemma":[0.9842767,0.01382671,0.0003862718,0.001279737,0.0001490185,0.000081611],"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":[0.00001852006,0.0002507328,0.000003046711,0.0001070714,0.001120396,4.455627e-7,0.00139913,0.00004797594,1.416647e-7,0.6747513,0.3219177,0.000383506],"study_design_scores_gemma":[0.0003773457,0.000335245,0.0000261941,0.00003840743,0.008680697,0.00002383616,0.005209259,0.1488845,0.00001016858,0.7482654,0.08745619,0.0006927731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001517136,0.00002092882,0.6528052,0.0002779983,0.00003681466,0.0007187292,0.0004578625,0.00005820566,0.3456227],"genre_scores_gemma":[0.000237908,0.00000771642,0.6358289,0.0001151729,0.0002446892,0.0001307238,0.0004908072,0.00001768157,0.3629265],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2344615,"threshold_uncertainty_score":0.7302092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6511593301755464,"score_gpt":0.519684449392007,"score_spread":0.1314748807835394,"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."}}