Linganisha mbinu

	Muundo Imara wa Markov	Uchambuzi Imara wa Usikivu
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2005	1990s–2000s
Mwanzilishi	Nilim & El Ghaoui; Iyengar	Saltelli, A. and colleagues
Aina	Robust probabilistic model	Simulation-based robustness assessment pipeline
Chanzo asilia	Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798.	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
Majina mbadala	RMM, Robust Markov Chain, Uncertain Markov Model, Interval Markov Model	RSA, Robust SA, Sensitivity Analysis under Uncertainty, Uncertainty-robust sensitivity analysis
Zinazohusiana	4	3
Muhtasari	A Robust Markov Model applies robustness principles to Markov chains by replacing single-point transition probabilities with uncertainty sets, then optimizing against the worst-case realization. Originally developed for robust Markov decision processes in operations research, it is used wherever transition rates are estimated with noise or are subject to adversarial variation, ensuring decisions remain safe across the full uncertainty range.	Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty.