方法证据记录
Bayesian Agent-Based Modeling
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Agent-Based Modeling — Parameter Estimation and Uncertainty Quantification for Agent-Based Models
分类方法记录 · process-pipeline / simulation
- Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803. · DOI 10.1371/journal.pcbi.1002803
- Grazzini, J., Richiardi, M. (2015). Estimation of agent-based models by simulated minimum distance. Journal of Economic Dynamics and Control, 51, 148-165. · DOI 10.1016/j.jedc.2014.10.006
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