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| Model Markov× | Simulasi Monte Carlo× | |
|---|---|---|
| Bidang≠ | Simulasi | Pengambilan Keputusan |
| Keluarga≠ | Process / pipeline | MCDM |
| Tahun asal≠ | 1906 | 1949 |
| Pencetus≠ | Andrei Markov | Metropolis, N., Ulam, S. |
| Tipe≠ | Probabilistic state-transition model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Sumber perintis≠ | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alias≠ | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process | — |
| Terkait≠ | 5 | 0 |
| Ringkasan≠ | 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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