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- W4366459877 abstract "Performing inference in Bayesian models requires sampling algorithms to draw samples from the posterior. This becomes prohibitively expensive as the size of data sets increase. Constructing approximations to the posterior which are cheap to evaluate is a popular approach to circumvent this issue. This begs the question of what is an appropriate space to perform approximation of Bayesian posterior measures. This manuscript studies the application of Bayes Hilbert spaces to the posterior approximation problem. Bayes Hilbert spaces are studied in functional data analysis in the context where observed functions are probability density functions and their application to computational Bayesian problems is in its infancy. This manuscript shall outline Bayes Hilbert spaces and their connection to Bayesian computation, in particular novel connections between Bayes Hilbert spaces, Bayesian coreset algorithms and kernel-based distances." @default.
- W4366459877 created "2023-04-22" @default.
- W4366459877 creator A5041100313 @default.
- W4366459877 date "2023-04-18" @default.
- W4366459877 modified "2023-10-14" @default.
- W4366459877 title "Bayes Hilbert Spaces for Posterior Approximation" @default.
- W4366459877 doi "https://doi.org/10.48550/arxiv.2304.09053" @default.
- W4366459877 hasPublicationYear "2023" @default.
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