Reading: Ki, C., & Terhorst, J. (2022). Variational phylodynamic inference using pandemic-scale data. Molecular Biology and Evolution, 39(8), msac154. https://doi.org/10.1093/molbev/msac154.
Previous papers we have read this semester have focused on likelihood-based or full Bayesian inference for a increasingly general model classes. Practical data analysis also requires paying attention to computational scalability, in order to learn effectively from the large phylogenies nowadays available for some important pathogens. This paper develops a scalable method. Combining scalability with other desiderata is an ongoing research direction.
As before, you can get your class participation credit by submitting ahead of class, in writing, a few sentences describing (i) something you got out of reading this paper; (ii) something you got stuck on trying to understand. You can email this to ionides@umich.edu and kingaa@umich.edu with subject “700 participation”.
Note that each class is graded with one point for attendance and one for participation.