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| Deterministic Dynamic Programming× | Markovmodell× | |
|---|---|---|
| Ämnesområde | Simulering | Simulering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1957 | 1906 |
| Upphovsperson≠ | Richard E. Bellman | Andrei Markov |
| Typ≠ | Exact sequential optimization algorithm | Probabilistic state-transition model |
| Ursprungskälla≠ | Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Alias | DDP, Deterministic DP, Classical Dynamic Programming, Bellman Dynamic Programming | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | Deterministic Dynamic Programming (DDP) is a mathematical optimization technique that decomposes a multi-stage decision problem into a sequence of simpler subproblems, solving them exactly when all system parameters — transition functions, costs, and rewards — are known with certainty. It guarantees a globally optimal policy via Bellman's principle of optimality. | 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. |
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