October 2015
Beginner to intermediate
168 pages
4h 11m
English
Content preview from Learning Bayesian Models with RBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Summary
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 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.