Methoden vergleichen

	Stochastic Dynamic Programming	Markov-Modell
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1957	1906
Urheber	Bellman, R.; formalized for stochastic settings by Puterman, M. L.	Andrei Markov
Typ	Sequential optimization under uncertainty	Probabilistic state-transition model
Wegweisende Quelle	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Aliasnamen	SDP, Markov Decision Process, MDP, Stochastic DP	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Verwandt	6	5
Zusammenfassung	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.