{"id":"W2152662914","doi":"10.48550/arxiv.1202.3746","title":"Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Estimator; Mathematics; Applied mathematics; Efficiency; Computation; Matching (statistics); Asymptotic analysis; Covariance matrix; Covariance; Mathematical optimization; Statistics; Algorithm","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":[0.0200387,0.0008742044,0.001565245,0.002703077,0.000559596,0.002634157,0.003626782,0.002596018,0.00130652],"category_scores_gemma":[0.1233619,0.0007684262,0.001125394,0.00209915,0.004223316,0.005847415,0.004166903,0.001944193,0.0003886103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001769389,"about_ca_system_score_gemma":0.001440007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009915115,"about_ca_topic_score_gemma":0.0008157654,"domain_scores_codex":[0.9844833,0.01189754,0.0004110321,0.0009083493,0.001996903,0.0003029921],"domain_scores_gemma":[0.9364514,0.0527062,0.003087785,0.006222003,0.001211308,0.0003212533],"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":[0.0001058015,0.00007206511,0.002904352,0.0001836012,0.0001753407,0.0001031319,0.0001610394,0.172136,0.001171699,0.7570821,0.001190284,0.06471474],"study_design_scores_gemma":[0.00002796927,0.0000379075,0.0006908225,0.0000351494,0.0000241024,0.0001293708,0.00002993753,0.5840415,0.001044773,0.4129353,0.0009710645,0.00003206955],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01914898,0.0008759132,0.977078,0.0006091027,0.00002332576,0.0000283689,0.0000463082,0.0001596678,0.002030269],"genre_scores_gemma":[0.6394471,0.001553584,0.3554182,0.0004946376,0.0002301918,0.0002389647,0.0003140749,0.0002935185,0.002009706],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0200387,"threshold_uncertainty_score":0.105976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07262688603340584,"score_gpt":0.2075216804054238,"score_spread":0.134894794372018,"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."}}