{"id":"W2616741364","doi":"10.5555/2946645.2946655","title":"Herded gibbs sampling","year":2016,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Gibbs sampling; Graphical model; Computer science; Convergence (economics); Probabilistic logic; Inference; Artificial intelligence; Algorithm; Bayesian probability","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":[0.0001787414,0.00008653027,0.0001300337,0.0001146451,0.000162355,0.00002846765,0.0008728634,0.00005392448,0.0001449715],"category_scores_gemma":[0.00003896521,0.00007865035,0.00006355131,0.000207884,0.0000727056,0.0006039439,0.0003211696,0.00007219359,0.0002139353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003909091,"about_ca_system_score_gemma":0.00004775985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001988765,"about_ca_topic_score_gemma":0.00005400419,"domain_scores_codex":[0.9991775,0.00006579942,0.00008720386,0.0003098916,0.0001901036,0.000169539],"domain_scores_gemma":[0.9990107,0.0001002009,0.0001285592,0.0005984475,0.0000827741,0.00007930843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000513309,0.0001053418,0.01269052,0.00005732452,0.00004957909,0.00002467908,0.002372477,0.00002215187,0.01960888,0.06107693,0.005464837,0.8984759],"study_design_scores_gemma":[0.002941662,0.0003477386,0.4191847,0.0003468564,0.00004008683,0.0000313791,0.001254932,0.007269992,0.0009840592,0.004292835,0.5624882,0.000817498],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0631376,0.00002700705,0.9232199,0.006333172,0.0001517439,0.00006873346,0.00001835945,0.0001872354,0.006856242],"genre_scores_gemma":[0.953958,0.00002262139,0.04203074,0.00007565556,0.00003050072,1.071245e-7,0.0000101978,0.000005659996,0.003866557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8976585,"threshold_uncertainty_score":0.3207268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02241718206315677,"score_gpt":0.2199852240423439,"score_spread":0.1975680419791871,"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."}}