{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004198426,0.001033978,0.001567264,0.0013455,0.0006822385,0.001297266,0.002037398,0.001174577,0.002332747],"category_scores_gemma":[0.0241698,0.0008830538,0.001018377,0.00146756,0.001908477,0.00167653,0.001885569,0.002249519,0.0006299756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109154,"about_ca_system_score_gemma":0.002095282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004043735,"about_ca_topic_score_gemma":0.005495137,"domain_scores_codex":[0.9959466,0.002547898,0.0001090619,0.0004221551,0.0007662385,0.0002080031],"domain_scores_gemma":[0.9890367,0.008483442,0.0004587757,0.001199357,0.0006152366,0.0002065537],"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":[0.0001766098,0.0001112776,0.001913995,0.000173368,0.0001825663,0.0001802581,0.0001510226,0.6725547,0.002169192,0.2182162,0.003296131,0.1008748],"study_design_scores_gemma":[0.00002871924,0.00001468118,0.00009908519,0.00001072111,0.00001236375,0.00002867873,0.000004788585,0.9381665,0.0004945124,0.06035015,0.0007806077,0.000009164612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002101758,0.00006477596,0.9970357,0.00005416608,0.00001496844,0.00002464177,0.00001821244,0.000199745,0.000485955],"genre_scores_gemma":[0.1979953,0.0002323068,0.7990233,0.0002304752,0.0001244337,0.0003732896,0.0002413271,0.0002261906,0.00155327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004198426,"threshold_uncertainty_score":0.02220368,"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."}}