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Think Bayes: Bayesian Statistics Made Simple (2012)

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Re: Think Bayes: Bayesian Statistics Made Simple (2012)

#2
Thanks for posting this. The Jupyter notebooks (and the fact Github has built-in support for them) really help illustrating the concepts.

The book I've used so far to study is "Probability and Statistics: The Science of Uncertainty", by Michael J. Evans and Jeffrey S. Rosenthal. This book is not being published anymore and is free in PDF form.

Re: Think Bayes: Bayesian Statistics Made Simple (2012)

#6
For those unclear on the concrete (rather than philosophical) difference between Bayesian and frequentist statistics in the first place, I hope it's not inappropriate for me to share this 5-minute example that I wrote a while back: https://news.ycombinator.com/item?id=11096129

Re: Think Bayes: Bayesian Statistics Made Simple (2012)

#7

For those unclear on the concrete (rather than philosophical) difference between Bayesian and frequentist statistics in the first place, I hope it's not inappropriate for me to share this 5-minute example that I wrote a while back: https://news.ycombinator.com/item?id=11096129

Another problem for the less familiar with the Bayes theorem is what is described as the "Bayesian trap", explained by the youtuber Veritasium: https://www.youtube.com/watch?v=R13BD8qKeTg

Re: Think Bayes: Bayesian Statistics Made Simple (2012)

#10
post #3

I regularly forget how Bayes works. Everytime that happens I browse up to that page: https://www.bayestheorem.net/ I love the way it’s explained there.

How Bayes kinda works, or how I see it.

Conditional probability (with some caveats that someone in the comments can fill in on):

    P(a,b) = P(b,a)
    P(a|b) * P(b) = P(b|a) * P(a)
    P(a|b) = P(b|a) * P(a) / P(b)
a can be model and b can be data so it becomes

    P(model | data) =
    P(data | model) * P(model) / P(data)
We have or can estimate the things on the right side. We want to ultimately get the thing on the left side.
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