পদ্ধতির তুলনা করুন

	অনিশ্চয়তার অধীনে ক্রমিক সিদ্ধান্ত গ্রহণ	Markov Model
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1957	1906
প্রবর্তক	Bellman, R.; formalized for stochastic settings by Puterman, M. L.	Andrei Markov
ধরন	Sequential optimization under uncertainty	Probabilistic state-transition model
মৌলিক উৎস	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
অপর নাম	SDP, Markov Decision Process, MDP, Stochastic DP	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
সম্পর্কিত	6	5
সারসংক্ষেপ	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.	A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.