Robustness as a criterion for selecting a probability distribution under uncertainty
© Springer International Publishing AG 2017. Often, we only have partial knowledge about a probability distribution, and we would like to select a single probability distribution ρ(x) out of all probability distributions which are consistent with the available knowledge. One way to make this selecti...
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th-cmuir.6653943832-408102017-09-28T04:11:31Z Robustness as a criterion for selecting a probability distribution under uncertainty Sriboonchitta S. Nguyen H. Kreinovich V. Kosheleva O. © Springer International Publishing AG 2017. Often, we only have partial knowledge about a probability distribution, and we would like to select a single probability distribution ρ(x) out of all probability distributions which are consistent with the available knowledge. One way to make this selection is to take into account that usually, the values x of the corresponding quantity are also known only with some accuracy. It is therefore desirable to select a distribution which is the most robust—in the sense the x-inaccuracy leads to the smallest possible inaccuracy in the resulting probabilities. In this paper, we describe the corresponding most robust probability distributions, and we show that the use of resulting probability distributions has an additional advantage: it makes related computations easier and faster. 2017-09-28T04:11:31Z 2017-09-28T04:11:31Z Book Series 1860949X 2-s2.0-85012885667 10.1007/978-3-319-50742-2_3 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012885667&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40810 |
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© Springer International Publishing AG 2017. Often, we only have partial knowledge about a probability distribution, and we would like to select a single probability distribution ρ(x) out of all probability distributions which are consistent with the available knowledge. One way to make this selection is to take into account that usually, the values x of the corresponding quantity are also known only with some accuracy. It is therefore desirable to select a distribution which is the most robust—in the sense the x-inaccuracy leads to the smallest possible inaccuracy in the resulting probabilities. In this paper, we describe the corresponding most robust probability distributions, and we show that the use of resulting probability distributions has an additional advantage: it makes related computations easier and faster. |
format |
Book Series |
author |
Sriboonchitta S. Nguyen H. Kreinovich V. Kosheleva O. |
spellingShingle |
Sriboonchitta S. Nguyen H. Kreinovich V. Kosheleva O. Robustness as a criterion for selecting a probability distribution under uncertainty |
author_facet |
Sriboonchitta S. Nguyen H. Kreinovich V. Kosheleva O. |
author_sort |
Sriboonchitta S. |
title |
Robustness as a criterion for selecting a probability distribution under uncertainty |
title_short |
Robustness as a criterion for selecting a probability distribution under uncertainty |
title_full |
Robustness as a criterion for selecting a probability distribution under uncertainty |
title_fullStr |
Robustness as a criterion for selecting a probability distribution under uncertainty |
title_full_unstemmed |
Robustness as a criterion for selecting a probability distribution under uncertainty |
title_sort |
robustness as a criterion for selecting a probability distribution under uncertainty |
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2017 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012885667&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40810 |
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