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| 베이지안 시나리오 분석× | 마르코프 모델× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2000s | 1906 |
| 창시자≠ | Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s) | Andrei Markov |
| 유형≠ | Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis | Probabilistic state-transition model |
| 원전≠ | Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231. DOI ↗ | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| 별칭 | BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| 관련 | 5 | 5 |
| 요약≠ | Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures. | A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling. |
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