Perturbation Analysis of Markov Chain Monte Carlo for Graphical Models
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866918123239636992 |
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| author | Lin, Na Liu, Yuanyuan Smith, Aaron |
| author_facet | Lin, Na Liu, Yuanyuan Smith, Aaron |
| contents | The basic question in perturbation analysis of Markov chains is: how do small changes in the transition kernels of Markov chains translate to chains in their stationary distributions? Many papers on the subject have shown, roughly, that the change in stationary distribution is small as long as the change in the kernel is much less than some measure of the convergence rate. This result is essentially sharp for generic Markov chains. In this paper we show that much larger errors, up to size roughly the square root of the convergence rate, are permissible for many target distributions associated with graphical models. The main motivation for this work comes from computational statistics, where there is often a tradeoff between the per-step error and per-step cost of approximate MCMC algorithms. Our results show that larger perturbations (and thus less-expensive chains) still give results with small error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14246 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Perturbation Analysis of Markov Chain Monte Carlo for Graphical Models Lin, Na Liu, Yuanyuan Smith, Aaron Probability The basic question in perturbation analysis of Markov chains is: how do small changes in the transition kernels of Markov chains translate to chains in their stationary distributions? Many papers on the subject have shown, roughly, that the change in stationary distribution is small as long as the change in the kernel is much less than some measure of the convergence rate. This result is essentially sharp for generic Markov chains. In this paper we show that much larger errors, up to size roughly the square root of the convergence rate, are permissible for many target distributions associated with graphical models. The main motivation for this work comes from computational statistics, where there is often a tradeoff between the per-step error and per-step cost of approximate MCMC algorithms. Our results show that larger perturbations (and thus less-expensive chains) still give results with small error. |
| title | Perturbation Analysis of Markov Chain Monte Carlo for Graphical Models |
| topic | Probability |
| url | https://arxiv.org/abs/2312.14246 |