Сравнение методов

	Байесовский анализ чувствительности	Байесовская динамическая оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1984–1994	1957 (Bellman DP); Bayesian extensions 1990s–2000s
Автор метода	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)
Тип	Uncertainty propagation and sensitivity quantification	Sequential optimization with Bayesian belief updating
Основополагающий источник	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.	Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267
Другие названия	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	BDP, Bayesian DP, Bayesian sequential optimization, Bayesian stochastic control
Связанные	5	4
Сводка	Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty.	Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal policy that explicitly accounts for both immediate rewards and the value of information gained through exploration.