{"id":"W3165295415","doi":"10.48550/arxiv.2105.11205","title":"Deconvolution density estimation with penalised MLE","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Smoothness; Sample size determination; Transformation (genetics); Statistics; Sample (material); Mathematics; Fourier transform; Blind deconvolution; Noise (video); Maximum likelihood; SIGNAL (programming language); Computer science; Applied mathematics; Algorithm; Artificial intelligence; Mathematical analysis; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004496601,0.001034773,0.001435938,0.001428322,0.0004887648,0.001658272,0.001956383,0.002453774,0.002046105],"category_scores_gemma":[0.02277476,0.001049826,0.0009962978,0.001167503,0.001902175,0.002800042,0.002894109,0.003203491,0.0009591783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008611472,"about_ca_system_score_gemma":0.001308368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002087452,"about_ca_topic_score_gemma":0.00194492,"domain_scores_codex":[0.9972787,0.001354201,0.0001244487,0.0004198765,0.0007137044,0.0001090882],"domain_scores_gemma":[0.9922668,0.005577921,0.000442732,0.0007793654,0.0008194793,0.000113685],"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":[0.000377273,0.00008830828,0.001544247,0.0004212052,0.0002260481,0.0001771729,0.0001955555,0.7255728,0.01088971,0.1046636,0.003861481,0.1519826],"study_design_scores_gemma":[0.0000157276,0.00001628673,0.000170726,0.00001613434,0.00001000561,0.00004436353,0.000005785574,0.9727304,0.002303306,0.02325348,0.001412763,0.00002095173],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001268063,0.0001092176,0.9981013,0.00008134789,0.00001392261,0.000009025665,0.00001676133,0.0001638094,0.0002366457],"genre_scores_gemma":[0.1374237,0.0004547742,0.8583277,0.0002186453,0.0001109668,0.0001276061,0.0002710313,0.0002879463,0.002777689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004496601,"threshold_uncertainty_score":0.02378058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04043738399059722,"score_gpt":0.1703050415324563,"score_spread":0.129867657541859,"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."}}