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Uigizaji wa Matukio ya Kujitenga kwa Mfumo wa Bayesian — Uundaji wa Mchakato wa Kistohastiki Ulio na Taarifa za Baada ya Uchanganuzi

Uigizaji wa Matukio ya Kujitenga kwa Mfumo wa Bayesian (BDES) huunganisha uhitimisho wa takwimu wa Bayesian na uigizaji wa matukio ya kujitenga. Imani za awali kuhusu vigezo vya mfumo — kama vile viwango vya huduma, nyakati za kuwasili, au uwezekano wa kushindwa — husasishwa na data iliyozingatiwa kupitia nadharia ya Bayes, na usambazaji unaofuata unaotokana huendesha moja kwa moja injini ya uigizaji. Muunganisho huu huwaruhusu waundaji kueneza uhakika wa aina zote mbili (aleatory na epistemic) kupitia miundo ya michakato inayoendeshwa na matukio.

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Vyanzo

  1. 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. link
  2. Pidd, M. (2004). Computer Simulation in Management Science (5th ed.). Wiley. ISBN: 9780470092781

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Bayesian Discrete-Event Simulation — Posterior-informed stochastic process modeling. ScholarGate. https://scholargate.app/sw/simulation/bayesian-discrete-event-simulation

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ScholarGateBayesian Discrete-Event Simulation (Bayesian Discrete-Event Simulation — Posterior-informed stochastic process modeling). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/simulation/bayesian-discrete-event-simulation · Seti ya data: https://doi.org/10.5281/zenodo.20539026