![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/D3DAB762-BEF1-459E-8EED-185294090425 | - |
| Identifier | https://doi.org/10.1007/s10614-022-10277-z | - |
| Identifier | https://link.springer.com/article/10.1007/s10614-022-10277-z | - |
| Language | en | - |
| Extent | 23 pages | en |
| Title | Exploring uncertainty, sensitivity and robust solutions in mathematical programming through Bayesian analysis | en |
| Creator | Tsionas, Mike | en |
| Creator | Philippas Dionisis | en |
| Creator | Zopounidis Konstantinos | en |
| Creator | Ζοπουνιδης Κωνσταντινος | el |
| Publisher | Springer | en |
| Content Summary | The paper examines the effect of uncertainty on the solution of mathematical programming problems, using Bayesian techniques. We show that the statistical inference of the unknown parameter lies in the solution vector itself. Uncertainty in the data is modeled using sampling models induced by constraints. In this context, the objective is used as prior, and the posterior is efficiently applied via Monte Carlo methods. The proposed techniques provide a new benchmark for robust solutions that are designed without solving mathematical programming problems. We illustrate the benefits of a problem with known solutions and their properties, while discussing the empirical aspects in a real-world portfolio selection problem. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2024-02-27 | - |
| Date of Publication | 2023 | - |
| Subject | Mathematical programming | en |
| Subject | Robustness analysis | en |
| Subject | Bayesian analysis | en |
| Subject | Monte Carlo | en |
| Bibliographic Citation | M. G. Tsionas, D. Philippas and C. Zopounidis, “Exploring uncertainty, sensitivity and robust solutions in mathematical programming through Bayesian analysis,” Comput. Econ., vol. 62, no. 1, pp. 205–227, June 2023, doi: 10.1007/s10614-022-10277-z. | en |
Copyright © DIAS 2013
