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Bajezijansko modelovanje zasnovano na agentima×Simulacija Monte Karlo×
OblastSimulacijaDonošenje odluka
PorodicaProcess / pipelineMCDM
Godina nastanka2000s–2010s1949
TvoracSunnaker et al. / Grazzini & Richiardi (among key contributors)Metropolis, N., Ulam, S.
TipSimulation calibration and inference frameworkRobustness wrapper — Monte Carlo uncertainty propagation
Temeljni izvorSunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803. DOI ↗Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Drugi naziviBayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation
Srodne50
SažetakBayesian 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.MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateUporedite metode: Bayesian Agent-Based Modeling · MONTE-CARLO-SIMULATION. Preuzeto 2026-06-15 sa https://scholargate.app/sr/compare