Deep Bootstrap for Bayesian Inference

Understand

For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior.

• We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function.
• We survey existing work on both Bayesian parametric inference with Gibbs posteriors as well as Bayesian non-parametric inference.
• We then highlight recent bootstrap computational approaches to approximating loss-driven posteriors.