ScholarGate
Asystent

Porównaj metody

Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.

Bayesowska symulacja zdarzeń dyskretnych×Symulacja zdarzeń dyskretnych (DES)×
DziedzinaSymulacjaSymulacja
RodzinaProcess / pipelineProcess / pipeline
Rok powstania2000s–2010s1960s (formalized); modern computational form from 1970s onward
TwórcaDeveloped across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010sBanks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)
TypHybrid simulation-inference frameworkStochastic process simulation
Źródło pierwotneOnggo, 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. link ↗Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127
Inne nazwyBayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulationDES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
Pokrewne64
PodsumowanieBayesian 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.Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.
ScholarGateZbiór danych
  1. v1
  2. 2 Źródła
  3. PUBLISHED
  1. v1
  2. 2 Źródła
  3. PUBLISHED

Przejdź do wyszukiwania Pobierz slajdy

ScholarGatePorównaj metody: Bayesian Discrete-Event Simulation · Discrete-Event Simulation. Pobrano 2026-06-17 z https://scholargate.app/pl/compare