विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| सिमुलेशन-सहायता प्राप्त क्रॉस-सेक्शनल अनुसंधान× | मोंटे कार्लो सिमुलेशन× | |
|---|---|---|
| क्षेत्र≠ | अनुसंधान अभिकल्प | निर्णयन |
| परिवार≠ | Process / pipeline | MCDM |
| उद्भव वर्ष≠ | 2000s–2010s (consolidated as a named hybrid approach) | 1949 |
| प्रवर्तक≠ | Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues) | Metropolis, N., Ulam, S. |
| प्रकार≠ | Quantitative hybrid research design | Robustness wrapper — Monte Carlo uncertainty propagation |
| मौलिक स्रोत≠ | Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| उपनाम≠ | simulation-enhanced cross-sectional study, hybrid simulation cross-sectional design, cross-sectional simulation study, SACSR | — |
| संबंधित≠ | 3 | 0 |
| सारांश≠ | Simulation-assisted cross-sectional research combines the one-time, population-wide snapshot of a classic cross-sectional survey with computational simulation — such as agent-based modelling or Monte Carlo methods — to extend what can be inferred from data collected at a single point in time. Empirical cross-sectional data calibrate the simulation, which then explores counterfactuals, rare subgroups, or dynamic processes that the survey alone cannot reveal. | 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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