What are relatively simple simulations that succeed with an irrational probability? What are relatively simple simulations that succeed with an irrational probability?
Let me break down this question.
Relatively simple simulations
By "relatively simple" I mean only algorithms that involve only—

*

*unbiased random bits (and thus discrete uniform variates), and

*integer arithmetic.

The algorithm may:

*

*Use Flajolet's "geometric bag" technique to build up continuous uniform variates and to sample a probability equal to a uniform random variate.

*Use rational arithmetic, but only as a last resort.

*Generate Poisson random variates, since there is a way to do so using only integer arithmetic and/or coins with unknown success probability.

The algorithm may not:

*

*Use "multiprecision interval arithmetics or [...] functions of high complexity as primitives" (Flajolet et al. 2010).

*Make direct use of square root or transcendental functions or constants.

*Rely on "sequences of approximation functions of increasing complexity" (Flajolet et al. 2010), except as a last resort.

Succeed with an irrational probability

*

*The algorithm's expected value must equal a constant, namely an irrational number in (0, 1).

*If the algorithm outputs $X$, then it must be that $\mathbb{E}[X]$ is the desired constant.

*The desired irrational constant is allowed to depend on one or more parameters.

There are two ways the algorithm could "succeed with an irrational probability".

*

*It could return either 0 or 1 with probability equal to the constant.

*Or it could produce a value $r\ge 1$; then the algorithm could return a Bernoulli($1/r$) variate.

I ask for irrational probabilities because the rational case is quite trivial; if the rational probability is known there is no need for algorithms like the one I'm asking for. Rather, it's enough to compare a uniform($d$) random variate with $n$, where $d$ and $n$ are the denominator and numerator.
Examples

*

*1 divided by the golden ratio.

*1/π.

*$e^{-1}$.

*The Euler–Mascheroni constant (even though it's not known to be irrational).

*The frog problem with negative steps.  Here, the expected number of iterations turns out to be an irrational number in a far from obvious way, which is desirable for this question. (Indeed, the more implicit the algorithm's use of the irrational constant, the better.)

Remarks

*

*The algorithm or method should have been published in a research article or book, and should be different from the ones I already have in my pages on Bernoulli factory algorithms or more arbitrary-precision algorithms, especially the section "Algorithms for Specific Constants". My literature searches have shown that algorithms I'm seeking are quite hard to find.

*My interest is not to compute irrational constants to high precision, but rather to have the algorithm serve as a "black box", a coin that "shows heads" with an irrational probability and in an exact manner; e.g., to serve as input coins to other so-called "Bernoulli Factory" algorithms that turn a Bernoulli($\lambda$) probability into an exact Bernoulli($f(\lambda)$) probability.

References

*

*Flajolet, P., Pelletier, M., Soria, M., "On Buffon machines and numbers", arXiv:0906.5560v2 [math.PR], 2010.


I think the details may be somewhat different
for each 'desired irrational constant', but
here is a strategy that may work for many such
constants.
Here is a simple algorithm that estimates $\pi/4,$ the area in the unit square beneath
the quarter unit-circle with center at the origin.
set.seed(2021)
x = runif(10^6); y = runif(10^6)
mean(y <= sqrt(1-x^2))
[1] 0.785459
pi/4
[1] 0.7853982

Can you find a function in a suitable square or rectangle that
bounds an area equal to each of your desired irrational constants (or a simple function thereof)?
Figure with only 50,000 points in the unit square for clarity.

B = 50000; x = runif(B);  y = runif(B)
plot(x, y, pch=".")
blue = (y <= sqrt(1-x^2))
points(x[blue], y[blue], col="blue", pch=".")

