A Further Related Work

Motivated by the behavior of Bayesian inference in misspecified models Grün-wald et al. ( 2017); Jansen ( 2013) extensively studied the so called "generalized" Bayesian inference, However, these works consider only "warm posteriors" Grünwald et al. ( 2017) the prior favours simple models, hence it is beneficial to put more weight onto the prior and use warm posterior. Finally, we mention the work of Bhattacharya et al. ( 2019), in which the authors develop fractional posteriors with the goal of CIFAR-10, have been collected and curated. The Street View House Numbers dataset ( Netzer et al., 2011), which is divided into a training In CIFAR-10 ( Krizhevsky and Hinton, 2009), labellers followed strict guidelines to ensure high quality labelling of the images. In particular, labellers were instructed that "it's worse to include one that shouldn't be included than to exclude one. In this section we review the basics of (SG)-MCMC inference.