Comparar métodos

	Simulación Bayesiana de Eventos Discretos	Simulación Híbrida de Eventos Discretos Basada en Agentes
Campo	Simulación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	2000s–2010s	2000s
Autor original	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
Tipo	Hybrid simulation-inference framework	Hybrid simulation paradigm
Fuente seminal	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.
Alias	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
Relacionados	6	4
Resumen	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.