Сравнение методов

	Байесовское линейное программирование	Байесовская динамическая оптимизация
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1970s–1980s	1957 (Bellman DP); Bayesian extensions 1990s–2000s
Автор метода	Integrated from Dantzig (LP) and Zellner/Bayesian econometrics traditions	Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)
Тип	Optimization under Bayesian uncertainty	Sequential optimization with Bayesian belief updating
Основополагающий источник	Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. ISBN: 9780691059136	Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267
Другие названия	BLP, Bayesian LP, Bayesian stochastic linear programming, prior-posterior LP	BDP, Bayesian DP, Bayesian sequential optimization, Bayesian stochastic control
Связанные	6	4
Сводка	Bayesian Linear Programming (BLP) integrates Bayesian statistical inference with classical linear programming to handle uncertainty in model parameters such as objective function coefficients, constraint coefficients, or right-hand-side values. Instead of treating parameters as fixed or governed by worst-case bounds, BLP uses prior beliefs updated by data to form posterior distributions, which then guide the LP formulation and solution, producing decisions that are optimal in a probabilistic, data-informed sense.	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.