{"id":"W2116501114","doi":"10.7202/602194ar","title":"Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes","year":2009,"lang":"fr","type":"article","venue":"L Actualité économique","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université Laval","funders":"","keywords":"Markov chain Monte Carlo; Mathematics; Monte Carlo method; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001883506,0.00058129,0.0006712973,0.0002410887,0.0003501721,0.0008212843,0.001274343,0.0004810346,0.000192144],"category_scores_gemma":[0.0001488995,0.0006086979,0.000347707,0.0004123629,0.0003616233,0.001698873,0.0002478251,0.0004874681,0.0001047331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245954,"about_ca_system_score_gemma":0.0004678464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005457227,"about_ca_topic_score_gemma":0.0004566469,"domain_scores_codex":[0.9962072,0.0008925921,0.0007316039,0.001033704,0.00007853848,0.001056403],"domain_scores_gemma":[0.9972165,0.0006218867,0.0003302129,0.00115441,0.0001748378,0.0005022124],"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":[0.00001111513,0.0001679717,0.00008006012,0.00007712274,0.0000331863,0.00001075138,0.003597954,0.00002257066,0.000589858,0.5634187,0.0006584426,0.4313323],"study_design_scores_gemma":[0.0003197753,0.000221503,0.002709043,0.0002050385,0.00004127581,0.0001547918,0.00005960983,0.01337105,0.005164554,0.732188,0.2448935,0.0006718937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01153378,0.007612958,0.9266837,0.02846553,0.0004024375,0.0007511138,0.00005741236,0.0003483585,0.02414473],"genre_scores_gemma":[0.1648336,0.005203325,0.7956701,0.0098952,0.0008810514,0.0002290323,0.00001477263,0.00005196413,0.02322092],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4306604,"threshold_uncertainty_score":0.9996364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05582102159134963,"score_gpt":0.3105426650046177,"score_spread":0.2547216434132681,"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."}}