On perfect sampling: ROCFTP with Metropolis-multishift coupler
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913797992611840 |
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| author | Nabipoor, Majid |
| author_facet | Nabipoor, Majid |
| contents | ROCFTP is a perfect sampling algorithm that employs various random operations, and requiring a specific Markov chain construction for each target. To overcome this requirement, the Metropolis algorithm is incorporated as a random operation within ROCFTP. While the Metropolis sampler functions as a random operation, it isn't a coupler. However, by employing normal multishift coupler as a symmetric proposal for Metropolis, we obtain ROCFTP with Metropolis-multishift. Initially designed for bounded state spaces, ROCFTP's applicability to targets with unbounded state spaces is extended through the introduction of the Most Interest Range (MIR) for practical use. It was demonstrated that selecting MIR decreases the likelihood of ROCFTP hitting $MIR^C$ by a factor of (1 - ε), which is beneficial for practical implementation. The algorithm exhibits a convergence rate characterized by exponential decay. Its performance is rigorously evaluated across various targets, and tests ensure its goodness of fit. Lastly, an R package is provided for generating exact samples using ROCFTP Metropolis-multishift. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On perfect sampling: ROCFTP with Metropolis-multishift coupler Nabipoor, Majid Computation Statistics Theory Methodology ROCFTP is a perfect sampling algorithm that employs various random operations, and requiring a specific Markov chain construction for each target. To overcome this requirement, the Metropolis algorithm is incorporated as a random operation within ROCFTP. While the Metropolis sampler functions as a random operation, it isn't a coupler. However, by employing normal multishift coupler as a symmetric proposal for Metropolis, we obtain ROCFTP with Metropolis-multishift. Initially designed for bounded state spaces, ROCFTP's applicability to targets with unbounded state spaces is extended through the introduction of the Most Interest Range (MIR) for practical use. It was demonstrated that selecting MIR decreases the likelihood of ROCFTP hitting $MIR^C$ by a factor of (1 - ε), which is beneficial for practical implementation. The algorithm exhibits a convergence rate characterized by exponential decay. Its performance is rigorously evaluated across various targets, and tests ensure its goodness of fit. Lastly, an R package is provided for generating exact samples using ROCFTP Metropolis-multishift. |
| title | On perfect sampling: ROCFTP with Metropolis-multishift coupler |
| topic | Computation Statistics Theory Methodology |
| url | https://arxiv.org/abs/2504.12872 |