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We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximumlikelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a samplingbased algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Ourdoi:10.1109/iccv.2005.130 dblp:conf/iccv/VedaldiJFS05 fatcat:rqqxze36bzd5dn3zftfmup2vba