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| Стохастичен Марков модел× | Анализ на чувствителността× | |
|---|---|---|
| Област≠ | Симулационно моделиране | Вземане на решения |
| Семейство≠ | Process / pipeline | MCDM |
| Година на възникване≠ | 1993 | 2004 |
| Създател≠ | Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others) | Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. |
| Тип≠ | Probabilistic state-transition model with Monte Carlo uncertainty propagation | Robustness wrapper — parameter / weight perturbation sensitivity indices |
| Основополагащ източник≠ | Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338. DOI ↗ | Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester DOI ↗ |
| Други названия≠ | Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model | — |
| Свързани≠ | 6 | 0 |
| Резюме≠ | 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. | SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateНабор от данни ↗ |
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