Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Stochastic Sensitivity Analysis× | Stohastiskā diskrēto notikumu simulācija× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s–2000s | 1960s–1970s |
| Autors≠ | Saltelli, A. et al.; Claxton, K. et al. (health economics stream) | Banks, Carson, Nelson, Nicol; Law, A. M. |
| Tips≠ | Probabilistic uncertainty quantification technique | Stochastic simulation model |
| Pirmavots≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975 | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 |
| Citi nosaukumi | PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty. | Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals. |
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