{"id":"W2051257992","doi":"10.1109/sam.2012.6250552","title":"Distributed posterior Cram&amp;#x00E9;r-Rao lower bound for nonlinear sequential Bayesian estimation","year":2012,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Estimator; Fisher information; Bayesian probability; Upper and lower bounds; Algorithm; Wireless sensor network; Sensor fusion; Mathematics; Artificial intelligence; Computer network; 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":[0.002587736,0.001154004,0.0009599742,0.0009113507,0.0004587935,0.00131046,0.001557558,0.001502,0.003694422],"category_scores_gemma":[0.01626402,0.0006273782,0.0005850432,0.001337781,0.001552165,0.00194408,0.001266158,0.00229183,0.002032302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001755367,"about_ca_system_score_gemma":0.002416102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00467218,"about_ca_topic_score_gemma":0.005038702,"domain_scores_codex":[0.9979522,0.0005749853,0.00007135742,0.000384332,0.0008982134,0.0001190427],"domain_scores_gemma":[0.9958103,0.00289187,0.000223386,0.0004231073,0.0005911979,0.00006016111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001275835,0.00005233581,0.0006685032,0.0002523156,0.0000616476,0.00008881268,0.0001103994,0.6228038,0.005914415,0.1663591,0.006148121,0.197413],"study_design_scores_gemma":[0.00001066712,0.00002245276,0.0001872474,0.00002220296,0.00001141666,0.00003777892,0.000006514164,0.9561129,0.001681922,0.03961403,0.002276978,0.00001599172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005437498,0.0002330319,0.9982277,0.00007306629,0.00001407378,0.000008626889,0.00002762377,0.0001194901,0.0007527457],"genre_scores_gemma":[0.1900137,0.002249413,0.7988083,0.0002710365,0.0003832428,0.0003016905,0.0005599973,0.0003350647,0.007077568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00467218,"threshold_uncertainty_score":0.01368541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02679463969054214,"score_gpt":0.2884846367645102,"score_spread":0.2616899970739681,"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."}}