Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)
Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
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.