I&#x27;m not at all qualified to talk about this. Without further ado:<p>Bayesianism has the concept of an A_p distribution, which is roughly &quot;the probability that the probability is p&quot;.<p>My A_p distribution for this coin would be (almost) 50% on 0 and 50% on 1, because I don&#x27;t think it&#x27;s possible to bias a coin except by making both faces the same. If I didn&#x27;t think that, it might be proportional to something like (p-1&#x2F;2)^2, because more extreme biases seem more likely.<p>In both cases, my probability of seeing a heads is 1&#x2F;2, because that&#x27;s the mean of the A_p distribution. But after flipping the coin once, my A_p distribution updates, and now I can give a probability of seeing the next toss come up heads.<p>I haven&#x27;t read the whole of it, but relevant chapter from Jaynes: <a href="http:&#x2F;&#x2F;www-biba.inrialpes.fr&#x2F;Jaynes&#x2F;cc18i.pdf" rel="nofollow">http:&#x2F;&#x2F;www-biba.inrialpes.fr&#x2F;Jaynes&#x2F;cc18i.pdf</a>