It relieves each procedure's learning difficulty and thus leads to superior performance.

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.

The " cold posterior effect " (CPE) in Bayesian deep learning describes the uncom-forting observation that the predictive performance of Bayesian neural networks

Appendix A.1 defines the augmented state-space model that formalizes the dynamics of the Gauss-Markov processes introduced in Section 3.1. Appendix A.2 provides the equations for prediction and update steps of the extended Kalman filter in such a setup, which is The block-diagonal structure is due to the independent dynamics of the prior processes. In the experiments presented in Sections 5.2 and 5.3 we model the latent contact rate This section is concerned with the exact steps that make up the algorithm summarized in Section 3.4. The stochastic differential equation defined in Eq. As detailed in Section 3, two different update steps are defined for two kinds of observations.