Review for NeurIPS paper: Generalised Bayesian Filtering via Sequential Monte Carlo

Weaknesses: - The authors choose to select \beta based on predictive accuracy. This is sensible, but what other approaches could also be used? And does it make sense to consider predictive accuracy on a separate training dataset? In the SMC community, people usually care more about the efficiency with which you can calculate the likelihood function (in order to estimate the parameters with particle MCMC), the accuracy of the filtered distribution, or ESS. Predictive accuracy is usually not a primary accuracy criterion so does it make sense to select \beta with this metric? From the simulation study, it appears that using predictive accuracy works well, but also seems to be consistently sub-optimal.