方法证据记录
Bayesian Discrete-Event Simulation
Bayesian Discrete-Event Simulation (BDES) integrates Bayesian statistical inference with discrete-event simulation. Prior beliefs about system parameters — such as service rates, arrival times, or failure probabilities — are updated with observed data via Bayes' theorem, and the resulting posterior distributions directly drive the simulation engine. This coupling allows modelers to propagate both aleatory and epistemic uncertainty through event-driven process models.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Discrete-Event Simulation — Posterior-informed stochastic process modeling
分类方法记录 · process-pipeline / simulation
- Onggo, B. S., & Kunc, M. (2016). Combining discrete-event simulation and Bayesian updating for incorporating evidence from real-world data. Journal of Simulation, 10(1), 1-12. · URL
- Pidd, M. (2004). Computer Simulation in Management Science (5th ed.). Wiley. · ISBN 9780470092781
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