This is probably the most comprehensive Bayesian course on the Internet today - his demos in R and Python, lecture notes, and videos are all excellent. This course uses the 3rd edition of the Bayesian Data Analysis book, available for free in PDF form.
For updated videos, check out his YouTube channel.Īki Vehtari from Aalto University in Finland released his popular Bayesian Data Analysis course online - you can now take it at your own pace. Richard is currently teaching a class called “Statistical Rethinking: A Bayesian Course” with all the materials including lecture videos available on GitHub. The example code had been ported to lots of languages including Stan, PyMC3, Julia, and more.
I don’t love the obscure chapter names (makes it hard to figure out what’s inside) but this is the kind of book I wish I had when I was learning statistics. In the book, he builds up to inference from probability and first principles and assumes only a basic background in math. Richard McElreath from the Max Planck Institute for Evolutionary Anthropology recently published the second edition of Statistical Rethinking. This is no longer the case, and so here are my top three resources for learning Bayes. One annoying gap at the time was the lack of online resources for learning Bayesian statistics. Re-reading it now, it still seems like pretty good advice, although it’s a bit too machine learning and Coursera heavy for my current tastes. In 2012 I wrote a couple of posts on how to learn statistics without going to grad school.
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