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We would like to thank all reviewers for their helpful feedback. Assigned_Reviewer_1 ------------------- We agree that investigating empirical convergence behavior as a function of problem size is an interesting direction for future work. This is, however, a difficult undertaking, since (1) selecting a suitable class of instances that can naturally be scaled in terms of size (n) is not always straightforward, and (2) diagnosing convergence of a MCMC algorithm in practice is known to be notoriously difficult. That is why, in this work, we elected to evaluate, and compare with the variational baseline, the accuracy of the sampler on previously-proposed practical instances. Assigned_Reviewer_2 ------------------- * Similarly to other probabilistic models, having a probabilistic facility location model allows us to quantify uncertainty about conclusions we draw using the model, and provides a principled way for learning such a model from data, with applications including document [1] and image [2] summarization.