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Communication Dans Un Congrès Année : 2017

Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling

Résumé

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.
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Dates et versions

hal-01713573 , version 1 (20-02-2018)

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  • HAL Id : hal-01713573 , version 1

Citer

Andrei-Cristian Bărbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet. Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling. Neural Information Processing Systems (NIPS) 2017, Dec 2017, Long Beach, CA, United States. pp.5020--5028. ⟨hal-01713573⟩
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