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An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk
A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. These...
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| Format: | Thesis |
| Language: | Eng |
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Department of Statistical Sciences
2024
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