  The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep
learning shows that, for posteriors with a temperature $T<1$, the resulting
posterior predictive could have better performances than the Bayesian posterior
($T=1$). As the Bayesian posterior is known to be optimal under perfect model
specification, many recent works have studied the presence of CPE as a model
misspecification problem, arising from the prior and/or from the likelihood
function. In this work, we provide a more nuanced understanding of the CPE as
we show that misspecification leads to CPE only when the resulting Bayesian
posterior underfits. In fact, we theoretically show that if there is no
underfitting, there is no CPE.
