{"id":"W1513781221","doi":"10.1023/a:1011094131273","title":"Rate of Convergence of the Gibbs Sampler in the Gaussian Case","year":2001,"lang":"en","type":"article","venue":"Mathematical Geology","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Applied mathematics; Mathematics; Covariance; Gaussian; Rate of convergence; Context (archaeology); Convergence (economics); Gibbs sampling; Covariance matrix; Fixed point; Mathematical optimization; Mathematical analysis; Computer science; Algorithm; Statistics; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02997048,0.001241666,0.002173023,0.003414121,0.002085917,0.004658518,0.0049783,0.003276933,0.009394805],"category_scores_gemma":[0.1541803,0.001330755,0.001855413,0.001935892,0.005430669,0.006797229,0.00610073,0.006012779,0.002009152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003206905,"about_ca_system_score_gemma":0.00380824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008357118,"about_ca_topic_score_gemma":0.004387422,"domain_scores_codex":[0.9924149,0.003490744,0.0003689945,0.001374013,0.001436567,0.0009148318],"domain_scores_gemma":[0.8491045,0.1272526,0.003423301,0.007435007,0.00970222,0.00308244],"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":[0.0006940577,0.0001114812,0.01409631,0.0004953041,0.000239262,0.0004212113,0.0011418,0.1474936,0.003861116,0.7864649,0.008308804,0.03667203],"study_design_scores_gemma":[0.0001058077,0.0001167643,0.00347868,0.0001835111,0.00009355498,0.0006053919,0.0002017383,0.6827542,0.002855772,0.3057286,0.003745605,0.0001303634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08717535,0.003638437,0.8890199,0.005036056,0.0003549418,0.0001692507,0.001031192,0.001126256,0.01244859],"genre_scores_gemma":[0.7822434,0.006037075,0.1747327,0.001679168,0.00107872,0.00104606,0.003289089,0.002302487,0.0275914],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02997048,"threshold_uncertainty_score":0.158501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147744630136737,"score_gpt":0.2533831049290888,"score_spread":0.2319056586277214,"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."}}