Generative neural networks for characteristic functions
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916395255595008 |
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| author | Brück, Florian |
| author_facet | Brück, Florian |
| contents | We provide a simulation algorithm to simulate from a (multivariate) characteristic function, which is only accessible in a black-box format. The method is based on a generative neural network, whose loss function exploits a specific representation of the Maximum-Mean-Discrepancy metric to directly incorporate the targeted characteristic function. The algorithm is universal in the sense that it is independent of the dimension and that it does not require any assumptions on the given characteristic function. Furthermore, finite sample guarantees on the approximation quality in terms of the Maximum-Mean Discrepancy metric are derived. The method is illustrated in a simulation study. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_04778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative neural networks for characteristic functions Brück, Florian Machine Learning Methodology We provide a simulation algorithm to simulate from a (multivariate) characteristic function, which is only accessible in a black-box format. The method is based on a generative neural network, whose loss function exploits a specific representation of the Maximum-Mean-Discrepancy metric to directly incorporate the targeted characteristic function. The algorithm is universal in the sense that it is independent of the dimension and that it does not require any assumptions on the given characteristic function. Furthermore, finite sample guarantees on the approximation quality in terms of the Maximum-Mean Discrepancy metric are derived. The method is illustrated in a simulation study. |
| title | Generative neural networks for characteristic functions |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2401.04778 |