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Articles (2021), Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83 (1), pp. 157-187

Gibbs flow for approximate transport with applications to Bayesian computation

HENG Jeremy , Doucet Arnaud, Pokern Yvo

We show here how to build a tractable approximation of a novel transport map. Even when this ordinary differential equation is time‐discretised and the full conditional distributions are numerically approximated, the resulting distribution of mapped samples can be efficiently evaluated and used as a proposal within sequential Monte Carlo samplers. We demonstrate significant gains over state‐of‐the‐art sequential Monte Carlo samplers at a fixed computational complexity on a variety of applications. Lien vers l'article

HENG, J., DOUCET, A. and POKERN, Y. (2021). Gibbs flow for approximate transport with applications to Bayesian computation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83(1), pp. 157-187.

Mots clés : #Markoc-chain, #Monte-Carlo, #mass-transport, #normalising-constants, #path-sampling