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This paper presents an algorithm for Monte Carlo fixed-lag smoothing instate-space models defined by a diffusion process observed through noisydiscrete-time measurements. Based on a particles approximation of the filteringand smoothing distributions, the method relies on a simulation technique ofconditioned diffusions. The proposed sequential smoother can be applied togeneral non linear and multidimensional models, like the ones used inenvironmental applications. The smoothing of a turbulent flow in ahigh-dimensional context is given as a practical example.

Monte Carlo fixed-lag smoothing in state-space models