পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Stochastic Scenario Analysis× | অনিশ্চয়তার অধীনে ক্রমিক সিদ্ধান্ত গ্রহণ× | |
|---|---|---|
| ক্ষেত্র | অনুকরণ | অনুকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1955–1980s | 1957 |
| প্রবর্তক≠ | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| ধরন≠ | Probabilistic scenario enumeration and evaluation | Sequential optimization under uncertainty |
| মৌলিক উৎস≠ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| অপর নাম | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis | SDP, Markov Decision Process, MDP, Stochastic DP |
| সম্পর্কিত≠ | 4 | 6 |
| সারসংক্ষেপ≠ | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
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