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• W2964014263 abstract "In this article, we present data-subsetting algorithms that allow for the approximate and scalable implementation of the Bayesian bootstrap. They are analogous to two existing algorithms in the frequentist literature: the bag of little bootstraps (Kleiner et al., 2014) and the subsampled double bootstrap (Sengupta et al., 2016). Our algorithms have appealing theoretical and computational properties that are comparable to those of their frequentist counterparts. Additionally, we provide a strategy for performing lossless inference for a class of functionals of the Bayesian bootstrap and briefly introduce extensions to the Dirichlet Process." @default.
• W2964014263 created "2019-07-30" @default.
• W2964014263 creator A5028533491 @default.
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• W2964014263 date "2020-06-01" @default.
• W2964014263 modified "2023-09-26" @default.
• W2964014263 title "Bayesian Bootstraps for Massive Data" @default.
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• W2964014263 doi "https://doi.org/10.1214/19-ba1155" @default.
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