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- W186151502 abstract "Standard Bayesian inference can behave suboptimally if the model is wrong. We present a modification of Bayesian inference which continues to achieve good rates with wrong models. Our method adapts the Bayesian learning rate to the data, picking the rate minimizing the cumulative loss of sequential prediction by posterior randomization. Our results can also be used to adapt the learning rate in a PAC-Bayesian context. The results are based on an extension of an inequality due to T. Zhang and others to dependent random variables." @default.
- W186151502 created "2016-06-24" @default.
- W186151502 creator A5079050279 @default.
- W186151502 date "2012-01-01" @default.
- W186151502 modified "2023-09-26" @default.
- W186151502 title "The Safe Bayesian" @default.
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- W186151502 doi "https://doi.org/10.1007/978-3-642-34106-9_16" @default.
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