  As the amount of economic and other data generated worldwide increases
vastly, a challenge for future generations of econometricians will be to master
efficient algorithms for inference in empirical models with large information
sets. This Chapter provides a review of popular estimation algorithms for
Bayesian inference in econometrics and surveys alternative algorithms developed
in machine learning and computing science that allow for efficient computation
in high-dimensional settings. The focus is on scalability and parallelizability
of each algorithm, as well as their ability to be adopted in various empirical
settings in economics and finance.
