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math.ST2026
Bayesian Adversarial Privacy
Cameron Bell, Timothy Johnston, Antoine Luciano +1
Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphases and aims. This work introduces a novel quantitative notion of…
math.ST2026
Persuasive Privacy
Joshua J Bon, James Bailie, Judith Rousseau +1
We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that a…
math.ST2014
Approximate Bayesian Computation in State Space Models
Gael M. Martin, Brendan P. M. McCabe, Worapree Maneesoonthorn +1
A new approach to inference in state space models is proposed, based on approximate Bayesian computation (ABC). ABC avoids evaluation of the likelihood function by matching observe…