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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.

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Bayesian Discrete-Event Simulation — Posterior-informed stochastic process modeling

Taksonoomiline meetodikirje · 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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Taxonomic bucketAgent-based Discrete-Event Simulationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Agent-Based Modelingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Markov Modelmachine-suggested · Relational suggestion, not evidence.Same method familyDiscrete-Event Simulationmachine-suggested · Relational suggestion, not evidence.See alsoMONTE-CARLO-SIMULATIONmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketStochastic Discrete-Event Simulationmachine-suggested · Relational suggestion, not evidence.