Порівняння методів

	Байєсівське агентне моделювання	Байєсівське мікросимуляційне моделювання
Галузь	Імітаційне моделювання	Імітаційне моделювання
Родина	Process / pipeline	Process / pipeline
Рік появи	2000s–2010s	1990s–2000s
Автор методу	Sunnaker et al. / Grazzini & Richiardi (among key contributors)	Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers
Тип	Simulation calibration and inference framework	Individual-level probabilistic simulation with Bayesian updating
Основоположне джерело	Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.	Williamson, P., Birkin, M., & Rees, P. H. (2000). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30(5), 785-816.
Інші назви	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation	Bayesian micro-simulation, BMS, Bayesian individual-level simulation, Probabilistic microsimulation
Пов'язані	5	6
Підсумок	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.	Bayesian Microsimulation combines individual-level simulation of heterogeneous populations with Bayesian statistical inference. Each synthetic individual follows a probabilistic life path, while model parameters are governed by prior beliefs updated with observed data. This approach is widely used in health technology assessment, public policy costing, and demographic projection, where uncertainty in both model inputs and structural assumptions must be formally quantified and propagated through to output estimates.