Bayesian Inference Under Model Misspecification

<jats:title>Abstract</jats:title> <jats:p>The likelihood input to a Bayesian analysis almost never exactly represents how the data were generated, calling into question the validity of posterior inferences. We review a variational interpretation of the Bayesian posterior as an alternative justification for its use under model misspecification, and consider the resulting implications on uncertainty quantification and parameter estimation. We then introduce a range of techniques that seek to obtain generalized Bayesian inferences that account for model misspecification.</jats:p>