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In Bayesian statistics, Pareto smoothed importance sampling refers to an approximation to leave-one-out cross-validation that stabilizes importance weights by fitting a generalized Pareto tail, with the tail shape flagging unreliable points. This meaning comes from Vehtari (2016) in Statistics and Computing.[1]
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