{"id":"W2148178414","doi":"10.1007/s11222-007-9028-9","title":"On population-based simulation for static inference","year":2007,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":207,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Markov chain Monte Carlo; Inference; Population; Markov chain; Bayesian inference; Computer science; Monte Carlo method; Sampling (signal processing); Statistics; Algorithm; Bayesian probability; Mathematics; Applied mathematics; Artificial intelligence; Sociology","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":[0.01023247,0.00215779,0.003989287,0.00333503,0.001987287,0.003525723,0.005666003,0.004449104,0.01067706],"category_scores_gemma":[0.07738449,0.002185663,0.002571576,0.004690396,0.004996705,0.006881056,0.005612247,0.007933136,0.003055393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002712554,"about_ca_system_score_gemma":0.003055881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009582723,"about_ca_topic_score_gemma":0.007815882,"domain_scores_codex":[0.9935508,0.004007274,0.0003048618,0.0006778956,0.001233329,0.0002258295],"domain_scores_gemma":[0.9494634,0.0431956,0.0008264663,0.003977313,0.001999015,0.0005381636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006343523,0.00006300511,0.0004675746,0.0001073286,0.00009045591,0.00009266779,0.0001135692,0.470874,0.000258743,0.4870739,0.002688191,0.03810719],"study_design_scores_gemma":[0.00001048881,0.000004503082,0.00003293691,0.00001746679,0.000008540801,0.00001938266,0.000004890642,0.7414511,0.00007673098,0.2573467,0.00101721,0.00001000584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004656703,0.0001954661,0.9980691,0.0001334643,0.00004355212,0.00001542855,0.0000220386,0.0001921576,0.0008632218],"genre_scores_gemma":[0.09134223,0.001283741,0.9003682,0.0004376214,0.0004654344,0.0006257975,0.0003619808,0.0007061266,0.004408839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01067706,"threshold_uncertainty_score":0.05411518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437224421377677,"score_gpt":0.3512340842917407,"score_spread":0.326861840077964,"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."}}