{"id":"W2364341470","doi":"10.1002/cjs.11343","title":"Likelihood inflating sampling algorithm","year":2017,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Sampling (signal processing); Algorithm; Computer science; Bayesian probability; Posterior probability; Gibbs sampling; Likelihood function; Importance sampling; Set (abstract data type); Monte Carlo method; Statistics; Mathematics; Artificial intelligence; Estimation theory","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":[0.006829014,0.0008419758,0.00153594,0.001901495,0.001054921,0.001930065,0.003137619,0.001440223,0.009398369],"category_scores_gemma":[0.03318185,0.0006999469,0.001322611,0.002169665,0.001858353,0.001988122,0.003214418,0.002819778,0.002251712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648706,"about_ca_system_score_gemma":0.002293825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003511847,"about_ca_topic_score_gemma":0.002463041,"domain_scores_codex":[0.9951693,0.00275794,0.0002194432,0.0006660144,0.0009268352,0.0002603729],"domain_scores_gemma":[0.98464,0.01021122,0.0007065503,0.002179201,0.001962759,0.0003002559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004081771,0.0001494332,0.007142602,0.0002311665,0.00018344,0.0002854966,0.000367662,0.3449739,0.002521194,0.3327179,0.01243285,0.2985862],"study_design_scores_gemma":[0.00004933826,0.00002305715,0.0003513866,0.00002531,0.00001545699,0.00009504279,0.00001620983,0.8977728,0.0008239305,0.09688985,0.003920667,0.00001681244],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004387493,0.00009835788,0.9928336,0.0001612115,0.00003898967,0.00006886778,0.00009774009,0.0004487275,0.001865076],"genre_scores_gemma":[0.1314307,0.0001334307,0.8632904,0.0002717683,0.0001462792,0.0004974552,0.0007448389,0.0003400305,0.003145101],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009398369,"threshold_uncertainty_score":0.03611571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1326593454697426,"score_gpt":0.3720022831719525,"score_spread":0.2393429377022099,"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."}}