方法对比
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| 贝叶斯马尔可夫模型× | 蒙特卡洛模拟× | |
|---|---|---|
| 领域≠ | 仿真 | 决策 |
| 方法族≠ | Process / pipeline | MCDM |
| 起源年份≠ | 1990s–2000s | 1949 |
| 提出者≠ | Briggs, A.; Sculpher, M.; and broader Bayesian statistics community | Metropolis, N., Ulam, S. |
| 类型≠ | Probabilistic state-transition simulation | Robustness wrapper — Monte Carlo uncertainty propagation |
| 开创性文献≠ | Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| 别名≠ | Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation | — |
| 相关≠ | 4 | 0 |
| 摘要≠ | A Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yielding posterior distributions over outcomes such as costs, life-years, or quality-adjusted life-years rather than single-point estimates. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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