Jacob Bernoulli married Judith Stupanus and they had six children, three boys and three girls ($\theta=0.5$ one might speculate). In repeating the sexual act at least six times with her husband Jacob, this 17 year old daughter of theology professor Christoph enacted in the flesh of her own uterus that even more useful probability distribution, the Binomial $P(X=k|\theta) = {n \choose k} \theta^k(1-\theta)^{n-k}$, which was also first documented by the owner of the penis which impregnated her so frequently, in the book Ars Conjectandi (1713). She may have contented herself that she delivered the maximum likelihood number of boys and girls to Jacob before he died ($P(X=3|0.5) = {6 \choose 3} 0.5^3(1-0.5)^3$) and this happy balancing act means I don't need to state whether we interpret $X$ as the number of boys she delivered or the number of girls.
It is noteworthy however that three of these children went on to be mathematicians in their own right, and quite unlike the situation with their gender, the distribution of mathematicians across his offspring's genders is as far away from the maximum likelihood as it is possible to get - all three mathematicians, not terribly surprisingly, were male.
However, in the paper "Measuring paternal discrepancy and its public health consequences" Volume 59 Issue 9, Bellis et.al. we find that these days, certainly for Swiss neighbours Germany, DNA tests show that between 12% and 21%, on average 16.8% of children were not the issue of the sperm of the pater familias. Let us engage in the art of conjecture, in honour of Jacob. Let us ask, what was the probability that all 6 of the little humans which emerged from uterus of Judith were all Jacob's.
It is with a certain deviant pleasure mixed with some anxiety that I say, let $X$ be the number of times a wife delivers a child who isn't the genetic descendant of her husband. And let us assume she issues six of her own children in this lifelong experiment of licentious lust. Let us first, respectfully, work out the probability that Jacob was father to all 6.
$P(X=0|0.168) = {6 \choose 0} 0.168^0(1-0.168)^6 = 33%$. And now let us turn to the event of $X>0$, which has a cumulative probability of 66.8%. Jacob might turn in his grave, like a logarithmic spiral but just like the Archimedean spiral which actually ended up on his tombstone by mistake, so too are the figures in this blog post. He only had two children. So he likely had a 70% chance of being the daddy.
All this slight of hand is by way of introduction to the idea that it is quite likely that no coin which has ever been made has the probability of heads and tails be equal at 0.5. Nor has any population of coins ever had a population mean, or even a sample mean of 0.5. We humans just don't care to look or measure hard enough. This is a function of the finitude of each of our times here on earth, a function too of the very approximate and crude tools available to us to measure things and of our desire to live in the dream of mathematical perfection. No coin has ever been fair or ever will be fair. As with love and death.
In the great Kantian divide of noumenal / phenomenal, we see the Bayesians happily reside in the phenomenal corner but my point here is that there may well be nothing we can truly (or probabilistically) say about the so called "real world".
I could also imagine a coin which had a tiny hollowed out channel in its body with a little droplet of mercury in the channel, which made the concept of a singular objective probability of heads even more difficult to imagine. Bayesians can chose to imagine the subjective probability they're measuring is a measure of their own state of relative ignorance of a radically real reality, but they can just as easily remain indifferent to any ontological commitment to the 'heads-ness' of any coin. Perhaps the heads-ness of every coin is forever unknowable and all we can do is present credibility intervals. But what a useful fable Jacob Bernoulli showed us how to tell ourselves. (I note in passing that his brother Johann had a further four children on top of Jacob's two, making six Bernoulli trials).
