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Exploring uncertainty, sensitivity and robust solutions in mathematical programming through bayesian analysis

Tsionas, Efthymios G.; Philippas, Dionisis; Zopounidis, … - In: Computational economics 62 (2023) 1, pp. 205-227

Persistent link: https://www.econbiz.de/10014327494

Multiobjective interacting particle algorithm for global optimization

Mete, Huseyin Onur; Zabinsky, Zelda B. - In: INFORMS journal on computing : JOC 26 (2014) 3, pp. 500-513

Persistent link: https://www.econbiz.de/10010399777

Importance sampling in stochastic programming : a Markov chain Monte Carlo approach

Parpas, Panos; Ustun, Berk; Webster, Mort; Tran, Quang Kha - In: INFORMS journal on computing : JOC 27 (2015) 2, pp. 358-377

Persistent link: https://www.econbiz.de/10011291297

Bayesian failure-rate modeling and preventive maintenance optimization

Belyi, Dmitriy; Popova, Elmira; Morton, David P.; … - In: European journal of operational research : EJOR 262 (2017) 3, pp. 1085-1093

Persistent link: https://www.econbiz.de/10011802470

A Bayesian stochastic Kriging optimization model dealing with heteroscedastic simulation noise for freeway traffic management

Chen, Xiqun Michael; He, Xiang; Xiong, Chenfeng; Zhu, Zheng - In: Transportation science : a journal of the Institute for … 53 (2019) 2, pp. 545-565

Persistent link: https://www.econbiz.de/10012022510

Metropolis-Hastings prefetching algorithms

Strid, Ingvar (contributor) - 2008 - Rev. December 2, 2009

Prefetching is a simple and general method for single-chain parallelisation of the Metropolis-Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of time. Improved Metropolis-Hastings prefetching algorithms are presented and evaluated. It is shown how to use...

Persistent link: https://www.econbiz.de/10003779724