A weighted Discrepancy Bound of quasi-Monte Carlo Importance Sampling
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912161579663360 |
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| author | Dick, Josef Rudolf, Daniel Zhu, Houying |
| author_facet | Dick, Josef Rudolf, Daniel Zhu, Houying |
| contents | Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_08115 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | A weighted Discrepancy Bound of quasi-Monte Carlo Importance Sampling Dick, Josef Rudolf, Daniel Zhu, Houying Computation Numerical Analysis 62F15, 11K45 Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain an explicit error bound in terms of the star-discrepancy for this method. |
| title | A weighted Discrepancy Bound of quasi-Monte Carlo Importance Sampling |
| topic | Computation Numerical Analysis 62F15, 11K45 |
| url | https://arxiv.org/abs/1901.08115 |