Approximate Slice Sampling for Bayesian Posterior Inference


Christopher DuBois, Anoop Korattikara, Max Welling, Padhraic Smyth ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:185-193, 2014.


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.

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