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For any \u03c00 and \u03c01, if one could obtain an analytical expression for a transport map from \u03c00 to \u03c01, then this could be straightforwardly applied to sample from any distribution. One would map draws from an easy-to-sample distribution \u03c00 to the target distribution \u03c01 using this transport map. Although it is usually impossible to obtain an explicit transport map for complex target distributions, we show here how to build a tractable approximation of a novel transport map. This is achieved by moving samples from \u03c00 using an ordinary differential equation with a velocity field that depends on the full conditional distributions of the target. 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.<\/jats:p>","DOI":"10.1111\/rssb.12404","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T17:31:28Z","timestamp":1611163888000},"page":"156-187","source":"Crossref","is-referenced-by-count":21,"title":["Gibbs Flow for Approximate Transport with Applications to Bayesian Computation"],"prefix":"10.1093","volume":"83","author":[{"given":"Jeremy","family":"Heng","sequence":"first","affiliation":[{"name":"ESSEC Business School \u00a0 Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnaud","family":"Doucet","sequence":"additional","affiliation":[{"name":"Department of Statistics \u00a0 , Oxford , UK"},{"name":"University of Oxford \u00a0 , Oxford , 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