Prior-to-posterior maps in Bayesian inverse problems, filtering, and joint state-parameter learning are pointwise globally Lipschitz, producing new non-asymptotic bounds on the error of approximate sequential Bayesian methods.
Burt, Carl Edward Rasmussen, and Mark van der Wilk
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A global Lipschitz stability perspective for understanding approximate approaches in Bayesian sequential learning
Prior-to-posterior maps in Bayesian inverse problems, filtering, and joint state-parameter learning are pointwise globally Lipschitz, producing new non-asymptotic bounds on the error of approximate sequential Bayesian methods.