Process / pipelineSimulation / optimization
贝叶斯蒙特卡洛模拟 — 基于先验信息的随机抽样用于不确定性量化
贝叶斯蒙特卡洛模拟将贝叶斯统计推断与蒙特卡洛抽样相结合，以传播复杂模型中的不确定性。它不从任意分布中抽取样本，而是通过贝叶斯定理将抽样条件化于观测数据和专家先验知识，从而产生既统计一致又概率可解释的后验不确定性估计。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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
- O'Hagan, A. (1987). Monte Carlo is fundamentally unsound. The Statistician, 36(2-3), 247-249. DOI: 10.2307/2348519 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Monte Carlo Simulation — Prior-informed stochastic sampling for uncertainty quantification. ScholarGate. https://scholargate.app/zh/simulation/bayesian-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.
Compare side by side →