Robust Markov Model
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.
Source record
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- Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798. · DOI 10.1287/opre.1050.0216
- Iyengar, G. N. (2005). Robust dynamic programming. Mathematics of Operations Research, 30(2), 257-280. · DOI 10.1287/moor.1040.0129
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