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- W4384464513 abstract "This is a review of asymptotic and non-asymptotic behaviour of Bayesian methods under model specification. In particular we focus on consistency, i.e. convergence of the posterior distribution to the point mass at the best parametric approximation to the true model, and conditions for it to be locally Gaussian around this point. For well specified regular models, variance of the Gaussian approximation coincides with the Fisher information, making Bayesian inference asymptotically efficient. In this review, we discuss how this is affected by model misspecification. In particular, we highlight contribution of Volodia Spokoiny to this area. We also discuss approaches to adjust Bayesian inference to make it asymptotically efficient under model misspecification." @default.
- W4384464513 created "2023-07-17" @default.
- W4384464513 creator A5064555528 @default.
- W4384464513 date "2023-01-01" @default.
- W4384464513 modified "2023-09-26" @default.
- W4384464513 title "Bernstein–von Mises Theorem and Misspecified Models: A Review" @default.
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- W4384464513 doi "https://doi.org/10.1007/978-3-031-30114-8_10" @default.
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