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| 확률적 민감도 분석× | 확률적 마르코프 모형× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s–2000s | 1993 |
| 창시자≠ | Saltelli, A. et al.; Claxton, K. et al. (health economics stream) | Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others) |
| 유형≠ | Probabilistic uncertainty quantification technique | Probabilistic state-transition model with Monte Carlo uncertainty propagation |
| 원전≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975 | Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338. DOI ↗ |
| 별칭 | PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis | Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model |
| 관련≠ | 5 | 6 |
| 요약≠ | Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty. | 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. |
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