I've published my book on car setup for racing:
https://www.amazon.com/dp/B09SBRGFYL
Not really a computer related subject, but anyway. Unfortunately, it's on the evil Amazon, but unfortunately there isn't a whole lot of other options at the moment :-(
I've been browsing at a used books store, and bought the book "Higher order Perl" by Mark-Jason Dominus. It's been published in 2003 but is still pretty amazing. It's about doing things that people normally associate with languages like Haskell, but in Perl. But it doesn't stop there. The book goes on to show an implementation of a parser infrastructure somewhat like ANTLR (but as you can imagine, with a lot less code), which is pretty mind-boggling, and then goes on to use it for a declarative drawing system that solves linear equations to determine the positions of the elements as described in a domain-oriented language.
The book can now be downloaded for free: https://hop.perl.plover.com/#free from the web site.
It kind of didn't occur to me initially to announce it here, but here we go: I've made the fully edited and typeset version of my book "The Practice of Parallel Programming" available as a PDF. It's exactly the same as the printed version, with a small intro to the Online Edition 1.0 added, and formatted to a page size that is more convenient for printing at home.
Available for download here:
https://sourceforge.net/projects/tpopp/files/tpopp-text/tpopp-pdf-210901.zip
I've stumbled upon the book "Introduction to probability" by Anderson, Seppalainen, and Valko. It's a college textbook.
Right in the second chapter it does a very good introduction into the Bayesian formula, deriving it from the descriptions of weights of events, in the same way as I struggled to reinvent on my own. And right there it also provides the "General version of Bayes' formula" for the enumerated events, that took me so much time to reinvent on my own. Well, another case of "everything is invented before us" :-) If only we knew, what books to read at the right time :-) On the other hand, I'm not sure if I would have recognized the importance of that formula if I didn't have a use for it in mind first.
In case if you wonder, here is the formula:
If B1...Bn are events that partition the sample space, then for any event A we have:
P(A) = sum from i=1 to n of (P(A & Bi)) = sum from i=1 to n of (P(A|Bi)*P(Bi))
Then
P(Bk|A) = P(A & Bk) / P(A) = P(A|Bk)*P(Bk) / (sum from i=1 to n of (P(A|Bi)*P(Bi)))
