Salīdzināt metodes

	Robustā Monte Carlo simulācija	Sekvenciālā Monte Karlo metode
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1990s–2000s	1993 (particle filter); 2006 (SMC samplers)
Autors	Saltelli, Rubinstein, and the uncertainty-quantification community	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tips	Robust simulation / uncertainty quantification	Sequential Bayesian computation
Pirmavots	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
Citi nosaukumi	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	SMC, particle filter, sequential importance resampling, SMC sampler
Saistītās	6	6
Kopsavilkums	Robust Monte Carlo simulation extends standard Monte Carlo by explicitly accounting for uncertainty in input distributions, model structure, or parameter assumptions. Rather than assuming a single fixed probability distribution for each input, the analyst considers a family of plausible distributions and evaluates how sensitive the output is to those choices, yielding conclusions that hold across a range of reasonable assumptions.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.