A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from Lévy Processes Without Gaussian Components
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916733551378432 |
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| author | Bernaciak, Dawid Griffin, Jim E. |
| author_facet | Bernaciak, Dawid Griffin, Jim E. |
| contents | We propose a general-purpose approximation to the Ferguson-Klass algorithm for generating samples from Lévy processes without Gaussian components. We show that the proposed method is more than 1000 times faster than the standard Ferguson-Klass algorithm without a significant loss of precision. This method can open an avenue for computationally efficient and scalable Bayesian nonparametric models which go beyond conjugacy assumptions, as demonstrated in the examples section. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from Lévy Processes Without Gaussian Components Bernaciak, Dawid Griffin, Jim E. Computation Applications We propose a general-purpose approximation to the Ferguson-Klass algorithm for generating samples from Lévy processes without Gaussian components. We show that the proposed method is more than 1000 times faster than the standard Ferguson-Klass algorithm without a significant loss of precision. This method can open an avenue for computationally efficient and scalable Bayesian nonparametric models which go beyond conjugacy assumptions, as demonstrated in the examples section. |
| title | A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from Lévy Processes Without Gaussian Components |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2407.01483 |