evil(x) -> not(do(x)) which equates to not(evil(x)) or not(do(x)).

The negation would be evil(x) and do(x) by DeMorgan's law.

If what you mean is all(x), evil(x) -> not(do(x))

then the negation would be exists(x), evil(x) and do(x).

Thanks for this reference; I found this paper interesting, but it is a satisfiability solver. Inherently it cannot quantify the probability of a subset of events, but it can find a probability assignment given a set of constraints. I.e. prove possibility. More usefully it can show that no such assignment is possible.

It's a typo. Its supposed to be a comma not a pipe, and read P(Q ⊆ X , ∀ x ∈ Q (x = 1)). I.e. Q is some subset of X and for all x in Q, x=1.

I think that's overly reductivist. In the general case DS operates on up to 2^M sets where M is the cardinality of the hypothesis space: worst case scenario. That's not true if hypotheses are hierarchical, or if evidence is frequently about the same set, or there just isn't enough evidence to fuse to get to 2^M.

In the worst case scenario there are efficient approximation methods which can be used.

Injective doesn’t mean bijective, and that seems obvious. That is, presumably very many inputs will map to the output “Yes”.

Afaict surjectivity was already a given before this paper, their contribution is the injectivity part (and thus invertibility)

Doing Advent of Code 2025 in SETL this year.

I think more often people cast the widest net and then filter what comes back based on “is this better than what I have”.

I’m not sure that the process the author describes is all that common in practice even if it is eminently sensible.

:-) I would have hired you!

That no one doing serious statistics uses QGis is false as evidenced both by community and sponsors. Try searching “who uses QGis”.

See the Jeffrey posterior section here:

https://en.m.wikipedia.org/wiki/Binomial_proportion_confiden...

The blog post uses a non informative Jeffrey prior.