Bayesian methodsBayesian / computational
稳健蒙特卡洛模拟
稳健蒙特卡洛模拟通过明确考虑输入分布、模型结构或参数假设中的不确定性，来扩展标准的蒙特卡洛方法。分析师不为每个输入假设一个单一的固定概率分布，而是考虑一族可能的分布，并评估输出对这些选择的敏感性，从而得出在一定合理假设范围内成立的结论。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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
- Rubinstein, R. Y. & Kroese, D. P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley. ISBN: 978-1118632161
如何引用本页
ScholarGate. (2026, June 3). Robust Monte Carlo Simulation. ScholarGate. https://scholargate.app/zh/bayesian/robust-monte-carlo-simulation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 自助法模拟仿真↔ compare
- 蒙特卡洛模拟决策↔ compare
- 稳健贝叶斯推断贝叶斯↔ compare
- 稳健粒子滤波器贝叶斯↔ compare
- 敏感性分析决策↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare