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- W3000230735 abstract "We study generalized Bayesian inference under misspecification, i.e. when the model is `wrong but useful'. Generalized Bayes equips the likelihood with a learning rate η. We show that for generalized linear models (GLMs), η-generalized Bayes concentrates around the best approximation of the truth within the model for specific η≠1, even under severely misspecified noise, as long as the tails of the true distribution are exponential. We then derive MCMC samplers for generalized Bayesian lasso and logistic regression, and give examples of both simulated and real-world data in which generalized Bayes outperforms standard Bayes by a vast margin." @default.
- W3000230735 created "2020-01-23" @default.
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- W3000230735 date "2019-10-21" @default.
- W3000230735 modified "2023-09-27" @default.
- W3000230735 title "Safe Bayesian Linear Regression" @default.
- W3000230735 hasPublicationYear "2019" @default.
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