Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Bayesiansk systemdynamik× | Bayesiansk Monte Carlo-simulering× | |
|---|---|---|
| Ämnesområde | Simulering | Simulering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2000s–2010s | 1987–1990s |
| Upphovsperson≠ | Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities | O'Hagan, A. and colleagues |
| Typ≠ | Simulation with probabilistic parameter learning | Simulation / uncertainty quantification |
| Ursprungskälla≠ | 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. DOI ↗ | O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992 |
| Alias | BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics | Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation |
| Närliggande≠ | 6 | 4 |
| Sammanfattning≠ | 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. | Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms. |
| ScholarGateDatamängd ↗ |
|
|