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حوزهشبیه‌سازیشبیه‌سازی
خانوادهProcess / pipelineProcess / pipeline
سال پیدایش1990s1957 (Bellman DP); Bayesian extensions 1990s–2000s
پدیدآورRios Insua, D. and colleaguesBellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)
نوعMulti-objective optimization under uncertaintySequential optimization with Bayesian belief updating
منبع بنیادینRios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267
نام‌های دیگرBGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal OptimizationBDP, Bayesian DP, Bayesian sequential optimization, Bayesian stochastic control
مرتبط64
خلاصهBayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulting probabilistic optimization problem to find solutions that satisfy multiple aspirational goals under 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.
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ScholarGateمقایسهٔ روش‌ها: Bayesian Goal Programming · Bayesian Dynamic Programming. بازیابی‌شده در 2026-06-15 از https://scholargate.app/fa/compare