{"id":"W1568113696","doi":"10.5281/zenodo.39011","title":"Marginalized Particle Filtering For Blind System Identification","year":2005,"lang":"en","type":"article","venue":"MacSphere (McMaster University)","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Particle filter; Autoregressive model; Finite impulse response; MIMO; Convolution (computer science); State-space representation; Impulse response; Algorithm; Computer science; Monte Carlo method; System identification; Bayesian probability; Blind signal separation; State space; Nonlinear system; Impulse (physics); Control theory (sociology); Infinite impulse response; Posterior probability; Channel (broadcasting); Kalman filter; Mathematics; Filter (signal processing); Artificial intelligence; Statistics; Digital filter; Telecommunications; Data modeling; Artificial neural network","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":[0.00125177,0.001004606,0.001089109,0.0008477273,0.0003874257,0.001024427,0.0006995758,0.001003095,0.004758356],"category_scores_gemma":[0.004663186,0.0005880965,0.000775966,0.0007516952,0.0007506268,0.001341608,0.0008243219,0.001801997,0.001716445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004171996,"about_ca_system_score_gemma":0.0008916176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002874606,"about_ca_topic_score_gemma":0.002756225,"domain_scores_codex":[0.9993687,0.0002357885,0.0000345743,0.0001087043,0.0002113848,0.0000408518],"domain_scores_gemma":[0.9989844,0.0005099357,0.00004043311,0.0001610629,0.0002749737,0.00002910788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006708679,0.0001124049,0.0007392845,0.0003821644,0.0002507003,0.0001759821,0.0001448815,0.21796,0.01931624,0.08223484,0.0254149,0.6525978],"study_design_scores_gemma":[0.00001749895,0.00004502139,0.0003802879,0.00001541168,0.00002942812,0.00005852765,0.000006645615,0.9750057,0.003852153,0.013994,0.006579524,0.00001582334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001351467,0.0007316005,0.9967852,0.0001107975,0.0002648271,0.000008579398,0.00003335482,0.0002788815,0.0004352788],"genre_scores_gemma":[0.1620326,0.002494987,0.8156211,0.0001782233,0.0006922082,0.0001291767,0.0007059893,0.0002977436,0.01784805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004758356,"threshold_uncertainty_score":0.01591831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780975504306106,"score_gpt":0.2366691191140066,"score_spread":0.2088593640709455,"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."}}