Salīdzināt metodes

	Baijesa sistēmdinamika	Stohastiskā sistēmu dinamika
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s	1980s–2000s
Autors	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers
Tips	Simulation with probabilistic parameter learning	Continuous stochastic simulation
Pirmavots	Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014.	Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159
Citi nosaukumi	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics
Saistītās	6	5
Kopsavilkums	Bayesian System Dynamics (BSD) integrates Bayesian statistical inference with causal stock-and-flow simulation models. Prior knowledge about model parameters is updated using observed time-series data to produce posterior distributions, which are then propagated through the simulation to yield probabilistic forecasts and policy evaluations rather than single deterministic trajectories.	Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios.