{"id":"W7008264660","doi":"","title":"Bayesian inference in networks","year":2011,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Particle filter; Inference; Unobservable; Importance sampling; Markov chain Monte Carlo; Approximate inference; Bayesian probability; Markov chain; Recursive Bayesian estimation; Overhead (engineering)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01227458,0.002509381,0.003562213,0.006553002,0.001886449,0.008040856,0.004848006,0.00469448,0.0106519],"category_scores_gemma":[0.04529992,0.002053292,0.002984321,0.005563321,0.005971204,0.009271228,0.00378314,0.005978213,0.002435537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004561957,"about_ca_system_score_gemma":0.003519612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01106144,"about_ca_topic_score_gemma":0.006882526,"domain_scores_codex":[0.9889648,0.005936971,0.0007227361,0.00213107,0.001936313,0.0003081481],"domain_scores_gemma":[0.9603084,0.03258944,0.002142161,0.002094361,0.002486705,0.0003789639],"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":[0.00004015755,0.00004374803,0.001673083,0.0005989055,0.0002949903,0.0001647776,0.0002485947,0.1381072,0.0002172939,0.7696729,0.009721136,0.0792172],"study_design_scores_gemma":[0.00002826803,0.00001519708,0.0002299601,0.0001695429,0.00003885762,0.00007213107,0.00004803146,0.2032285,0.0001380407,0.7791164,0.01687734,0.00003775257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00116446,0.002524149,0.9860061,0.002052433,0.0002650694,0.0001090635,0.0005790563,0.0003803388,0.006919328],"genre_scores_gemma":[0.1513048,0.01452758,0.8157566,0.001846562,0.002439184,0.001658787,0.002306349,0.000390738,0.0097694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01227458,"threshold_uncertainty_score":0.06491494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882506010200856,"score_gpt":0.2427719120299476,"score_spread":0.223946851927939,"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."}}