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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2012.07823 |
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| _version_ | 1866911853416808448 |
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| author | Brekelmans, Rob Masrani, Vaden Bui, Thang Wood, Frank Galstyan, Aram Steeg, Greg Ver Nielsen, Frank |
| author_facet | Brekelmans, Rob Masrani, Vaden Bui, Thang Wood, Frank Galstyan, Aram Steeg, Greg Ver Nielsen, Frank |
| contents | Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distributions between a tractable base and an unnormalized target. While AIS yields an unbiased estimator for any path, existing literature has been primarily limited to the geometric mixture or moment-averaged paths associated with the exponential family and KL divergence. We explore AIS using $q$-paths, which include the geometric path as a special case and are related to the homogeneous power mean, deformed exponential family, and $α$-divergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_07823 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Annealed Importance Sampling with q-Paths Brekelmans, Rob Masrani, Vaden Bui, Thang Wood, Frank Galstyan, Aram Steeg, Greg Ver Nielsen, Frank Machine Learning Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distributions between a tractable base and an unnormalized target. While AIS yields an unbiased estimator for any path, existing literature has been primarily limited to the geometric mixture or moment-averaged paths associated with the exponential family and KL divergence. We explore AIS using $q$-paths, which include the geometric path as a special case and are related to the homogeneous power mean, deformed exponential family, and $α$-divergence. |
| title | Annealed Importance Sampling with q-Paths |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2012.07823 |