{"id":"W2334908797","doi":"10.1109/mwscas.2014.6908538","title":"Joint noise distribution parameter estimation and LDPC decoding using variational Bayes","year":2014,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Factor graph; Estimation theory; Algorithm; Estimator; Decoding methods; Posterior probability; Joint probability distribution; Bayes' theorem; Low-density parity-check code; Computer science; Mathematics; Applied mathematics; Bayesian probability; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001895289,0.0009812173,0.001375752,0.0007457344,0.0005323252,0.001174341,0.001342505,0.00126679,0.001323986],"category_scores_gemma":[0.008349041,0.0008825593,0.0008499981,0.001030235,0.001497339,0.002206766,0.001329823,0.001738172,0.0003383773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155589,"about_ca_system_score_gemma":0.001958593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01238089,"about_ca_topic_score_gemma":0.007485933,"domain_scores_codex":[0.9988367,0.0005024931,0.00004828773,0.0002360156,0.0002810838,0.00009539567],"domain_scores_gemma":[0.9970397,0.002333799,0.0001808599,0.0001686954,0.000225493,0.00005137226],"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":[0.00005102572,0.00001774309,0.0004678703,0.00006512542,0.00004410559,0.00008091081,0.0001095801,0.9067349,0.001537934,0.0619288,0.0004683645,0.02849358],"study_design_scores_gemma":[0.00000421582,0.000004293734,0.00003911826,0.000003853252,0.00000362242,0.00001396387,0.000003969438,0.9829033,0.0003594282,0.01646427,0.000193726,0.000006167964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003414473,0.0001060479,0.9958479,0.00009816144,0.000008766106,0.00001204394,0.00002168132,0.00006427733,0.000426567],"genre_scores_gemma":[0.4526923,0.0006801118,0.5400966,0.0002080927,0.00009748986,0.0001538884,0.0002981572,0.0002285893,0.005544692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01238089,"threshold_uncertainty_score":0.02461767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119542647598378,"score_gpt":0.2448657343157786,"score_spread":0.2236703078397949,"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."}}