NIPS Proceedingsβ

Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling

Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Conference Event Type: Poster


We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution. Similarly to Hogwild methods, our approach does not target the original Gaussian distribution of interest, but an approximation to it. Contrary to Hogwild methods, a single parameter allows us to trade bias for variance. We show empirically that our method is very flexible and performs well compared to Hogwild-type algorithms.