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| 베이지안 마르코프 모형× | 몬테카를로 시뮬레이션× | |
|---|---|---|
| 분야≠ | 시뮬레이션 | 의사결정 |
| 계열≠ | Process / pipeline | MCDM |
| 기원 연도≠ | 1990s–2000s | 1949 |
| 창시자≠ | Briggs, A.; Sculpher, M.; and broader Bayesian statistics community | Metropolis, N., Ulam, S. |
| 유형≠ | Probabilistic state-transition simulation | Robustness wrapper — Monte Carlo uncertainty propagation |
| 원전≠ | Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| 별칭≠ | Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation | — |
| 관련≠ | 4 | 0 |
| 요약≠ | 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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