I would really like to read the full paper for reason #3, but it’s paywalled. The whole article feels like a big teaser with the real content locked away.
Even less friction than Sci-Hub is the Unpaywall browser extension http://unpaywall.org/products/extension which finds links to unpaywalled content matching paywalled journal articles.
For anyone who wants to dig a bit deeper: PRML by Bishop [0].
An amazing work as a general introduction to machine learning and Bayesian in general.
MacKay's book [1] is a bit more opinionated regarding Bayesian methods.
Even if you do not become purely Bayesian, it advances your understanding of to approach data quiet a bit compared to the generic "oh data, lets throw a NN at it".
About 40 years ago I was reading a business mathematics book and ran across a section that, as I recall, started out with a basic probabilities equation and derived the basic Bayesian equation in one step.
Am I remembering this right? If so could someone post it?
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[0] https://www.microsoft.com/en-us/research/people/cmbishop/prm... [1] http://www.inference.org.uk/mackay/itila/book.html
Am I remembering this right? If so could someone post it?
With the same definition, switching A and B roles, we have p(A, B) = p(B|A) p(A) (2)
Plug (2) into (1) and you have Bayes rule.