{"id":"W4309685037","doi":"10.1146/annurev-statistics-033121-110254","title":"Approximate Methods for Bayesian Computation","year":2022,"lang":"en","type":"article","venue":"Annual Review of Statistics and Its Application","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Approximate Bayesian computation; Bayesian probability; Bayesian inference; Inference; Variable-order Bayesian network; Statistical inference; Big data; Computation; Sampling (signal processing); Bayesian statistics; Machine learning; Artificial intelligence; Data mining; Algorithm; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004910797,0.001824216,0.002211572,0.0022737,0.0009682347,0.003744261,0.002800416,0.002510421,0.01659663],"category_scores_gemma":[0.03544446,0.001008525,0.001647111,0.003561387,0.002659525,0.004066566,0.003445639,0.005501403,0.005648774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0023374,"about_ca_system_score_gemma":0.002973676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005879162,"about_ca_topic_score_gemma":0.004865523,"domain_scores_codex":[0.9956713,0.002323223,0.0001954019,0.0004588463,0.001181905,0.0001693923],"domain_scores_gemma":[0.9872773,0.009735317,0.0004262776,0.001270248,0.001101176,0.0001896748],"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":[0.00003978012,0.00003475055,0.0003687769,0.0004313097,0.0001076798,0.00005368173,0.00009286328,0.07525256,0.0002524652,0.8047632,0.01368958,0.1049133],"study_design_scores_gemma":[0.00001585016,0.00001160893,0.00009158303,0.00009239327,0.00001710891,0.00003964765,0.00001619047,0.1676967,0.0001318236,0.8118809,0.01999236,0.00001386085],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0004724076,0.00511362,0.9858599,0.0009362358,0.0002613401,0.00004272913,0.0002147176,0.0003175794,0.006781465],"genre_scores_gemma":[0.08881023,0.01734144,0.8718624,0.001306287,0.002321806,0.0008617556,0.001229615,0.0009993175,0.01526716],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01659663,"threshold_uncertainty_score":0.05552131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04505196798231386,"score_gpt":0.4333828634473406,"score_spread":0.3883308954650267,"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."}}