Approximate Slice Sampling for Bayesian Posterior Inference
| Authors |
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| Publication date | 2014 |
| Journal | JMLR Workshop and Conference Proceedings |
| Event | Conference on Artificial Intelligence and Statistics (AISTATS 2014) |
| Volume | Issue number | 33 |
| Pages (from-to) | 185-193 |
| Organisations |
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| Abstract | In this paper, we advance the theory of large scale Bayesian posterior inference by introducing a new approximate slice sampler that uses only small mini-batches of data in every iteration. While this introduces a bias in the stationary distribution, the computational savings allow us to draw more samples in a given amount of time and reduce sampling variance. We empirically verify on three different models that the approximate slice sampling algorithm can significantly outperform a traditional slice sampler if we are allowed only a fixed amount of computing time for our simulations. |
| Document type | Article |
| Note | Artificial Intelligence and Statistics, 22-25 April 2014, Reykjavik, Iceland. Editors: Samuel Kaski, Jukka Corander |
| Language | English |
| Published at | http://jmlr.org/proceedings/papers/v33/dubois14.html |
| Downloads |
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