{"id":"W2963398635","doi":"","title":"Scalable MCMC for Mixed Membership Stochastic Blockmodels","year":2016,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Markov chain Monte Carlo; Scalability; Inference; Computer science; Mathematical optimization; Algorithm; Markov chain; Monte Carlo method; Applied mathematics; Mathematics; Artificial intelligence; Machine learning; Bayesian probability; Statistics","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":[0.0006105715,0.0002019799,0.0004500115,0.0001517891,0.0001818826,0.00001205256,0.0004166153,0.0001575262,0.0002684119],"category_scores_gemma":[0.0003088827,0.0001815156,0.0002759059,0.0001424188,0.0001610318,0.000209114,0.0001823466,0.00007757918,0.000001611474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009908557,"about_ca_system_score_gemma":0.0000698479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004917656,"about_ca_topic_score_gemma":0.0002182363,"domain_scores_codex":[0.9987062,0.0001149937,0.0001917303,0.0003607377,0.0002486227,0.0003776914],"domain_scores_gemma":[0.9978223,0.0009935816,0.0002172166,0.0005284189,0.0002707974,0.0001676307],"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":[0.004061885,0.001652419,0.0008127771,0.004205489,0.00173048,0.0001329622,0.01844308,0.0002080012,0.05387224,0.3768997,0.2416628,0.2963182],"study_design_scores_gemma":[0.06592271,0.005139591,0.001184422,0.007722176,0.004490624,0.0001818591,0.05427581,0.04575333,0.03286109,0.5414839,0.2325908,0.008393702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2083262,0.00004470824,0.7820073,0.001225446,0.0003024931,0.000617492,0.0001859311,0.0001214343,0.007168934],"genre_scores_gemma":[0.8422515,0.000009933276,0.1260872,0.00005444661,0.00009242913,0.000002763253,0.000007849661,0.00004486222,0.03144902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6559201,"threshold_uncertainty_score":0.740199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06906443377149273,"score_gpt":0.2839353260507567,"score_spread":0.214870892279264,"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."}}