Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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Example: Counting Github stars
Chapter 4 has a section titled Example: Counting Github stars:
What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million repositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO
Yeah, let's start! :) Maybe I could help with this to see if I understood any of the book up to this point. But what is the plan here? Just calculating the average of 6 million numbers is not so challenging. Is the idea to break it down by some factor?
How do we get that average (how do we get those 6 million numbers)? It would be interesting to post the average number of stars here.
This is one of the top results on Google for "average number of stars on github" on Google.