{"id":"W3121169149","doi":"10.22004/ag.econ.273594","title":"Bayesian Estimation of Dynamic Discrete Choice Models","year":2006,"lang":"en","type":"article","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Curse of dimensionality; Markov chain Monte Carlo; Dynamic programming; Mathematical optimization; Bayesian probability; Computer science; Grid; Posterior probability; Monte Carlo method; Algorithm; Markov chain; Bayes estimator; Structural estimation; Mathematics; Statistics; Artificial intelligence; Machine learning","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":[0.007798186,0.0008355009,0.001767509,0.002369144,0.0007168941,0.002063825,0.002947463,0.001493979,0.003610577],"category_scores_gemma":[0.04355616,0.001300772,0.001235104,0.002495354,0.001731241,0.003426079,0.002489476,0.003157672,0.0006046229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001771258,"about_ca_system_score_gemma":0.002706484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007234983,"about_ca_topic_score_gemma":0.007238974,"domain_scores_codex":[0.9920665,0.004924951,0.0002435469,0.0008715605,0.001625147,0.000268325],"domain_scores_gemma":[0.9837306,0.01294688,0.001322435,0.000933049,0.0008424927,0.0002245322],"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":[0.00003771405,0.0001003575,0.002224792,0.0001231992,0.0001978017,0.00006286649,0.0001592985,0.4493826,0.0004583868,0.4735657,0.002087765,0.0715995],"study_design_scores_gemma":[0.00002342503,0.00001558645,0.0003123354,0.00002244013,0.0000167698,0.00001955498,0.0000117007,0.8277309,0.0001585864,0.1696188,0.002053562,0.00001642359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002125069,0.0001027364,0.9966068,0.000185159,0.000014458,0.00002254038,0.00004310472,0.00006304959,0.0008370248],"genre_scores_gemma":[0.2809499,0.001070582,0.7130259,0.0002720685,0.0001765023,0.0005896842,0.0005615021,0.00011853,0.003235348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007798186,"threshold_uncertainty_score":0.04124123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01795592389960446,"score_gpt":0.2293437456315412,"score_spread":0.2113878217319367,"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."}}