First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors present a hierarchical extension of the IRM for network modelling using the key ideas from the Bayesian rose tree paper: 1) that the hierarchy is used to specify a mixture over consistent partitions of the nodes 2) that this hierarchy can be learnt using an efficient greedy agglomerative procedure. Qualitative results on the Sampson's monks dataset, and full NIPS dataset, and quantitative results on the NIPS-234 dataset are presented. The proposed inference is computational much cheaper than the IRM, whilst obtaining similar predictive performance. The paper is very well written and the exposition of the key ideas is clear.

We consider the problem of fitting variational posterior approximations using stochastic optimization methods.

We consider the problem of fitting variational posterior approximations using stochastic optimization methods.

Ideally, we would prefer an estimator that learns any distribution.

An essential aspect of any machine learning system is understanding what the model does not know.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Reaction to the author feedback: I cannot agree with point (3): The work of Siracusa III and Fisher (AISTATS 2009) does *not* assume the data are iid, and it allows the generating stucture vary (even if only in a small set of graphical models). While the present proposed method is, in some sense, even more flexible, I'd find it misleading to claim that the proposed method is the first to address the problem. The proposed method is specifically targeted to scenarios where the data generating graphical model may change relatively frequently. The performance of the method is demonstrated using both simulated and real data.

Questions on whether governments have acted promptly enough, and whether lockdown measures can be lifted soon, have since been central in public discourse. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential for addressing these questions and informing governments on future policy directions.

To solve this, we perform probabilistic reasoning over the depth of neural networks.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. COMMENTS BASED ON REVIEWER DISCUSSIONS AND AUTHOR REBUTTAL: I agree with the other reviewers that more could be done to constrain the specifics of the cue integration mechanism. However, I believe that if the data set is expanded, allowing the models to be better constrained, then the paper is appropriate and interesting for the NIPS community. I have left my quality score as it was, but I agree with the other reviewers that the paper merits a ``1'' rather than a ``2'' for impact score. ORIGINAL REVIEW: Summary: This paper extends an existing model for the perception of visual speed that uses a Bayesian observer model acting on the activity of independent spatiotemporal frequency channels. Previously, the model accounted for illusions of perceived speed by postulating the Bayes-optimal combination of noisy sensory representations with a prior for slow speeds.

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