In this last chapter of the book, we covered various frameworks to implement large-scale machine learning. These are very useful for Bayesian learning too. For example, to simulate from a posterior distribution, one could run a Gibbs sampling over a cluster of machines. We learned how to connect to Hadoop from R using the RHadoop package and how to use R with Spark using SparkR. We also discussed how to set up clusters in cloud services such as AWS and how to run Spark on them. Some of the native parallelization frameworks such as parallel and foreach functions were also covered.
The overall aim of this book was to introduce readers to the area of Bayesian modeling using R. Readers should have gained a good grasp of theory and concepts behind Bayesian machine learning models. Since the examples were mainly given for the purposes of illustration, I urge readers to apply these techniques to real-world problems to appreciate the subject of Bayesian inference more deeply.
