{"id":"W2050888643","doi":"10.1109/icassp.2013.6638899","title":"Maximum entropy estimation of the probability density function from the histogram using order statistic constraints","year":2013,"lang":"en","type":"article","venue":"","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Histogram; Probability density function; Differential entropy; Principle of maximum entropy; Mathematics; Density estimation; Maximum entropy spectral estimation; Maximum entropy probability distribution; Statistic; Order statistic; Entropy (arrow of time); Probability distribution; Kernel density estimation; Applied mathematics; Statistics; Estimator; Computer science; Artificial intelligence; Physics","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":[0.002243544,0.0004482387,0.0007271019,0.0011235,0.0004069277,0.00131214,0.00109395,0.0006168869,0.001586522],"category_scores_gemma":[0.0121513,0.000427639,0.0005838617,0.0007816234,0.00138268,0.002835426,0.001322351,0.001184983,0.0004573831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177783,"about_ca_system_score_gemma":0.001261051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543808,"about_ca_topic_score_gemma":0.002147861,"domain_scores_codex":[0.9994782,0.0001894492,0.00002933988,0.00009204145,0.0001607218,0.00005029263],"domain_scores_gemma":[0.9960436,0.003068344,0.0002221292,0.0002933554,0.0002876246,0.000085031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008684098,0.00005619171,0.002689458,0.0002124226,0.00005371662,0.000257544,0.0003155537,0.4989742,0.01558517,0.4030839,0.002135573,0.07654949],"study_design_scores_gemma":[0.000003722148,0.000007521255,0.0004532669,0.0000116712,0.00000357209,0.00004441298,0.00001237646,0.9257389,0.002179312,0.07098292,0.00054913,0.00001326527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01016162,0.00008531664,0.9881769,0.0001274164,0.000012715,0.00001608776,0.00003875628,0.00008691411,0.001294307],"genre_scores_gemma":[0.4935103,0.0007348036,0.4986007,0.0002059616,0.0001650665,0.0001547244,0.0004432716,0.0003189083,0.005866251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002543808,"threshold_uncertainty_score":0.01186514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570294826464782,"score_gpt":0.2302684641326077,"score_spread":0.2145655158679599,"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."}}