{"id":"W4409549920","doi":"10.54254/2753-8818/2025.22036","title":"Bayesian Inference for Dynamic Demand Forecasting and Inventory Optimization","year":2025,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Bayesian inference; Bayesian probability; Demand forecasting; Computer science; Dynamic Bayesian network; Econometrics; Economics; Artificial intelligence; Operations management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00201384,0.00008251036,0.0001198919,0.0001650755,0.0005211078,0.000260983,0.0003751809,0.00004169572,0.00001839108],"category_scores_gemma":[0.003934652,0.00005346374,0.00002289184,0.000857063,0.002142617,0.0002202304,0.0002445872,0.0000919025,6.112791e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001910956,"about_ca_system_score_gemma":0.0000660111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003217753,"about_ca_topic_score_gemma":0.00000356422,"domain_scores_codex":[0.9988399,0.00002750306,0.000229288,0.0004018647,0.0002865633,0.0002148872],"domain_scores_gemma":[0.9985141,0.0009557871,0.00006084385,0.0001735965,0.0002037675,0.00009189163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001384128,0.00000674828,0.0005853867,0.000005399937,8.534107e-7,1.341766e-7,0.00004615148,0.0001324112,0.0004697154,0.9365385,0.00004793907,0.06215299],"study_design_scores_gemma":[0.00005656297,0.00002131394,0.0004742763,0.00001844841,0.000003220885,0.000002382094,0.00002277351,0.5533107,0.0002224408,0.4457247,0.00009945976,0.00004372664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1646902,0.0002175227,0.8297677,0.002257354,0.00009612605,0.0003176296,0.000005043627,0.00005440436,0.002593978],"genre_scores_gemma":[0.9606693,0.00001988131,0.03893112,0.0001881485,0.000008755504,0.00001926941,0.000001070833,0.000002210288,0.0001601973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7959791,"threshold_uncertainty_score":0.7894561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03025792653076895,"score_gpt":0.3719128301183678,"score_spread":0.3416549035875989,"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."}}