However, this perspective fails to address the difficult data collection decisions that precede such modeling efforts. Doctors must select a set of tests to run. Survey designers must select a slate of questions to ask. Companies must select which datasets to purchase.

In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain.

For the larger discussion items, please find the detailed comments below. Additionally, the reviewers highlighted the importance of quantitative fits. We currently attempt to differentiate between these models using additional manipulations. R-learning may be advantageous for computation. Our work builds upon results in the field including Ref [2] This observation enabled us to pursue the hypothesis of the leaky estimate of average reward.

MWEM [5] to scale to far more measurements, and improves the accuracy of FEM [33].

Figure 1: A simple subsplit Bayesian network for a leaf set that contains 4 species A, B, C and D. This figure is adapted from Zhang and Matsen IV (2019). SBN (the one with a full and complete binary tree structure as shown in Figure 1) is good enough. The SBN framework also generalizes to unrooted trees, which are the most common type of phylogenetic trees. (Zhang and Matsen IV, 2018). Sampling from SBNs is also straightforward via ancestral sampling.

We show that VBPI-NF significantly improves upon the vanilla VBPI on a benchmark of challenging real data Bayesian phylogenetic inference problems.