Sammenlign metoder

	Bayesiansk diskret hændelsessimulering	Bayesiansk Agentbaseret Modellering
Fagområde	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2000s–2010s	2000s–2010s
Ophavsperson	Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s	Sunnaker et al. / Grazzini & Richiardi (among key contributors)
Type	Hybrid simulation-inference framework	Simulation calibration and inference framework
Oprindelig kilde	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.	Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.
Aliasser	Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation
Relaterede	6	5
Resumé	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.	Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations.