{"id":"W2080439709","doi":"10.1155/2013/176249","title":"Bayesian Sparse Estimation Using Double Lomax Priors","year":2013,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Prior probability; Smoothing; Latent variable; Bayesian inference; Inference; Bayesian probability; Autoregressive model; Mathematics; Computer science; Algorithm; Applied mathematics; Artificial intelligence; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001211944,0.0002205499,0.0002585458,0.0001920322,0.00002358393,0.00008075235,0.0001427971,0.0001143666,0.00009234122],"category_scores_gemma":[0.0000272045,0.0002213885,0.00004685816,0.0002424595,0.00001923449,0.0002826677,0.00004399799,0.0002335513,0.00007285722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079589,"about_ca_system_score_gemma":0.000006099141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002750681,"about_ca_topic_score_gemma":0.000001373035,"domain_scores_codex":[0.998922,0.000006811534,0.0003814174,0.0001656426,0.0001556349,0.000368504],"domain_scores_gemma":[0.9995509,0.00004933887,0.00002571218,0.0002668336,0.00002451342,0.00008264452],"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":[4.626089e-7,0.00002024315,0.00003275421,0.0002292522,0.00001080377,0.000003852633,0.0001821055,0.9809524,0.01632193,0.001327288,0.00008521609,0.0008336902],"study_design_scores_gemma":[0.0001851344,0.00000871373,0.00004460564,0.0004979129,0.000007529217,0.00002275716,0.00001243966,0.9781425,0.009640061,0.01115414,0.0000352843,0.0002489418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1209609,0.00008820263,0.8756173,0.00001494069,0.00009228494,0.0004591664,3.532591e-7,0.00097588,0.001790915],"genre_scores_gemma":[0.8938987,0.000005482249,0.105915,0.000007017503,0.00002993317,0.00006498234,0.000001652012,0.00006299256,0.00001422617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7729377,"threshold_uncertainty_score":0.902796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923914469937144,"score_gpt":0.226409346284543,"score_spread":0.2071702015851715,"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."}}