方法对比
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| 随机马尔可夫模型× | 随机动态规划× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1993 | 1957 |
| 提出者≠ | Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| 类型≠ | Probabilistic state-transition model with Monte Carlo uncertainty propagation | Sequential optimization under uncertainty |
| 开创性文献≠ | Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338. DOI ↗ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| 别名 | Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model | SDP, Markov Decision Process, MDP, Stochastic DP |
| 相关 | 6 | 6 |
| 摘要≠ | A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
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