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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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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