{"id":"W2129436741","doi":"10.1109/tsp.2007.896091","title":"An Information Geometric Approach to ML Estimation With Incomplete Data: Application to Semiblind MIMO Channel Identification","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"MIMO; Expectation–maximization algorithm; Algorithm; Computer science; Estimation theory; Channel (broadcasting); Maximization; Gaussian; Range (aeronautics); Identification (biology); Probability distribution; Iterative method; Mathematics; Mathematical optimization; Maximum likelihood; Statistics; Engineering","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":[0.002914299,0.001295985,0.00122873,0.001340774,0.0004570862,0.001244075,0.001304313,0.001191554,0.001518306],"category_scores_gemma":[0.01407003,0.0006016796,0.000844197,0.001230729,0.002265621,0.002782909,0.00233605,0.001610864,0.0006122625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005669195,"about_ca_system_score_gemma":0.0009331466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005303576,"about_ca_topic_score_gemma":0.0005162191,"domain_scores_codex":[0.9981471,0.000965736,0.00008066314,0.0002325378,0.000502229,0.00007166286],"domain_scores_gemma":[0.9935403,0.004487402,0.0004927411,0.0008438884,0.0005425402,0.00009305857],"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":[0.00008326005,0.00004315521,0.00038853,0.0001482002,0.00007123416,0.0001618982,0.0001682632,0.7209036,0.004135535,0.1336227,0.001334264,0.1389394],"study_design_scores_gemma":[0.000009741178,0.00004268431,0.0000985898,0.0000132293,0.000008769875,0.0001320879,0.0000156319,0.9402519,0.002401814,0.05529078,0.001709339,0.00002537197],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005888535,0.00004789283,0.9989935,0.0000633399,0.000008706857,0.000005392992,0.000007750215,0.00005778378,0.0002267296],"genre_scores_gemma":[0.1320604,0.0006447954,0.8651698,0.0002183718,0.0001664066,0.0001189521,0.0001377694,0.0001209598,0.001362566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002914299,"threshold_uncertainty_score":0.01541245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02682861038915697,"score_gpt":0.2879524184325664,"score_spread":0.2611238080434094,"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."}}