{"id":"W2116800676","doi":"10.48550/arxiv.1301.3890","title":"Monte Carlo Inference via Greedy Importance Sampling","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Monte Carlo method; Inference; Sampling (signal processing); Gibbs sampling; Computer science; Slice sampling; Importance sampling; Rejection sampling; Sampling distribution; Statistical inference; Algorithm; Greedy algorithm; Hybrid Monte Carlo; Mathematical optimization; Artificial intelligence; Mathematics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001229851,0.0001597599,0.000151719,0.0001038667,0.0001867384,0.0001154471,0.0009852122,0.00006209505,0.00009445088],"category_scores_gemma":[0.00003937523,0.0001661841,0.00007528252,0.0005806897,0.00005390945,0.0008212112,0.0003012159,0.0002516546,0.0003979118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004920417,"about_ca_system_score_gemma":0.00003895606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001097237,"about_ca_topic_score_gemma":0.00004934618,"domain_scores_codex":[0.9988597,0.00005524097,0.0001274911,0.0005452005,0.00007519429,0.0003371749],"domain_scores_gemma":[0.9988949,0.00009278463,0.0001069243,0.0006305301,0.0001094199,0.0001654405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001213117,0.0001930532,0.4039119,0.00004849849,0.0001028219,0.0003945031,0.001061755,0.3669001,0.0007917891,0.1699826,0.0009216875,0.05567916],"study_design_scores_gemma":[0.000232517,0.00004644446,0.02341071,0.00001303631,0.00000637433,0.000006446036,0.00003156137,0.9677529,0.00003361773,0.007632893,0.0005868038,0.0002466688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4438585,0.00002283004,0.5542808,0.0001177425,0.0001389398,0.00007886469,5.827175e-7,0.0002052706,0.001296449],"genre_scores_gemma":[0.9912686,0.00001601337,0.006270188,0.0001622402,0.000052411,6.767094e-7,6.898833e-7,0.000008395441,0.002220738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6008528,"threshold_uncertainty_score":0.6776791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05242060392084918,"score_gpt":0.1883839919087255,"score_spread":0.1359633879878764,"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."}}