Robust and Private Bayesian Inference


We examine the robustness and privacy of Bayesian inference, under assumptions on the prior, and with no modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome spaces and distribution families. We then prove bounds on the robustness of the posterior, introduce a posterior… (More)
DOI: 10.1007/978-3-319-11662-4_21



Citations per Year

Citation Velocity: 36

Averaging 36 citations per year over the last 3 years.

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