方法对比
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| 贝叶斯马尔可夫模型× | 贝叶斯敏感性分析× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 1984–1994 |
| 提出者≠ | Briggs, A.; Sculpher, M.; and broader Bayesian statistics community | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) |
| 类型≠ | Probabilistic state-transition simulation | Uncertainty propagation and sensitivity quantification |
| 开创性文献≠ | Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629 | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ |
| 别名 | Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis |
| 相关≠ | 4 | 5 |
| 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. | Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty. |
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