Linganisha mbinu

	Uigizaji wa Matukio ya Kujitenga kwa Mfumo wa Bayesian	Uigaji wa Matukio Tofauti Unaotegemea Wakala
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2000s–2010s	2000s
Mwanzilishi	Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s	Hybridization formalized by multiple authors; Siebers & Aickelin, Lagergren & Buckley among key contributors
Aina	Hybrid simulation-inference framework	Hybrid simulation paradigm
Chanzo asilia	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.	Lagergren, J. H., & Buckley, E. (2010). A hybrid approach to simulation: Combining agent-based and discrete event simulation. Proceedings of the 2010 Winter Simulation Conference, pp. 170–181. IEEE.
Majina mbadala	Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation	AB-DES, Hybrid ABM-DES, Agent-DES, Hybrid Agent-Based Discrete-Event Simulation
Zinazohusiana	6	4
Muhtasari	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.	Agent-based discrete-event simulation (AB-DES) is a hybrid modeling paradigm that couples autonomous agent behavior with an event-driven execution engine. It captures the decision-making heterogeneity of individual entities while maintaining the precise, time-stamped flow control of discrete-event simulation, making it suitable for complex systems where both individual agency and process sequencing matter.