{"id":"W4320920238","doi":"10.3390/e25020355","title":"Change-Point Detection in a High-Dimensional Multinomial Sequence Based on Mutual Information","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Science Foundation of Anhui Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multinomial distribution; Statistic; Test statistic; Sequence (biology); Mathematics; Null hypothesis; Mutual information; Statistics; Statistical hypothesis testing; Change detection; Position (finance); Point (geometry); Algorithm; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003144387,0.0003931464,0.001124389,0.002407705,0.0004376511,0.0008528726,0.0008760552,0.0007889144,0.0006643789],"category_scores_gemma":[0.01587756,0.0002325793,0.0005682069,0.00135315,0.001338013,0.001881281,0.001379888,0.0006374269,0.0001441912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005520404,"about_ca_system_score_gemma":0.0005194251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009111137,"about_ca_topic_score_gemma":0.0007005555,"domain_scores_codex":[0.9975168,0.0008634832,0.00024314,0.0005372346,0.000693599,0.0001458064],"domain_scores_gemma":[0.9886194,0.008721906,0.001266411,0.0004243522,0.0006908145,0.0002769657],"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":[0.00141273,0.0003519179,0.1209224,0.0006383376,0.0005624967,0.001365228,0.001237293,0.3765862,0.03597002,0.06088564,0.001557245,0.3985105],"study_design_scores_gemma":[0.00001802216,0.0001698842,0.02035806,0.00003128562,0.0000418483,0.0003378003,0.0000936778,0.9592869,0.004370838,0.01473214,0.0005041786,0.00005530607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2321767,0.0003862327,0.7659983,0.0001965585,0.00003445588,0.00004903631,0.0001478247,0.0002186811,0.0007922419],"genre_scores_gemma":[0.9390291,0.0001657169,0.05997671,0.00004943293,0.00005407262,0.00007574393,0.0002535417,0.00001878746,0.0003768881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003144387,"threshold_uncertainty_score":0.01662928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127788758769987,"score_gpt":0.3860078408767802,"score_spread":0.2732289649997815,"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."}}