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Improvements and generalizations of Stochastic Knapsack and Markovian Bandits approximation algorithms

Ma, Will - In: Mathematics of operations research 43 (2018) 3, pp. 789-812

Persistent link: https://www.econbiz.de/10011914355

Penalty-based algorithms for the stochastic obstacle scene problem

Aksakalli, Vural; Ari, Ibrahim - In: INFORMS journal on computing : JOC 26 (2014) 2, pp. 370-384

Persistent link: https://www.econbiz.de/10010362436

The fragility of optimized bandit algorithms

Fan, Lin; Glynn, Peter W. - In: Operations research 73 (2025) 6, pp. 3173-3198

Persistent link: https://www.econbiz.de/10015551487

A new improvement scheme for approximation methods of probability density functions

Takahashi, Akihiko; Tsuzuki, Yukihiro - In: The journal of computational finance 19 (2016) 4, pp. 73-94

Persistent link: https://www.econbiz.de/10011603189

Probability bounds for polynomial functions in random variables

He, Simai; Jiang, Bo; Li, Zhening; Zhang, Shuzhong - In: Mathematics of operations research 39 (2014) 3, pp. 889-907

Persistent link: https://www.econbiz.de/10010402949

Exact Algorithms for the Multinomial Counts : Maximum, Minimum, Range and Sums of the Largest Order Statistics

Bonetti, Marco - 2018

Starting from a neglected work by Rappeport (1968), we re-propose an exact algorithm to compute the distribution of the maximum of a multinomial random vector under the hypothesis of equiprobability. We then show how to compute the exact probabilities of the sum of the J largest order statistics...

Persistent link: https://www.econbiz.de/10012932699