{"id":"W1589822290","doi":"10.1002/cjs.11196","title":"On the empirical efficiency of local MCMC algorithms with pools of proposals","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; Université de Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Metropolis–Hastings algorithm; Markov chain Monte Carlo; Mathematics; Algorithm; Bayesian probability; Computer science; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.04564375,0.001206645,0.002297189,0.003145991,0.001702918,0.002873197,0.004413267,0.00241227,0.004304539],"category_scores_gemma":[0.2064848,0.001329005,0.001226868,0.002802965,0.004537555,0.005001136,0.004454058,0.003166596,0.0009719117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002088368,"about_ca_system_score_gemma":0.002778019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006424781,"about_ca_topic_score_gemma":0.004868219,"domain_scores_codex":[0.984637,0.01214398,0.0003628689,0.0009964445,0.001438281,0.0004215378],"domain_scores_gemma":[0.725173,0.2490712,0.004804743,0.01411915,0.005391119,0.001440885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007553602,0.0001663437,0.009441612,0.0002356063,0.0002902649,0.000187701,0.0005109653,0.7168683,0.001108959,0.2049092,0.002536655,0.06298897],"study_design_scores_gemma":[0.00005430921,0.00005386176,0.0005410991,0.00004669841,0.00002430608,0.00004238922,0.00003726578,0.9546475,0.0005506533,0.04357265,0.0004061422,0.00002319966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0613535,0.001498498,0.9322126,0.0008198342,0.00003874974,0.0001537492,0.0001022346,0.0005394961,0.00328138],"genre_scores_gemma":[0.6313329,0.0009016854,0.3631215,0.0003212095,0.000137136,0.0006004936,0.0003664005,0.0004666097,0.002752013],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04564375,"threshold_uncertainty_score":0.2413902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04159148816363465,"score_gpt":0.2636385319355158,"score_spread":0.2220470437718811,"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."}}