Bayes and Bayesian Inference
The problem considered by Bayes in Proposition 9 of his essay, "An Essay towards solving a Problem in the Doctrine of Chances", is the posterior distribution for the parameter a (the success rate) of the binomial distribution.
What is "Bayesian" about Proposition 9 is that Bayes presented it as a probability for the parameter . That is, not only can one compute probabilities for experimental outcomes, but also for the parameter which governs them, and the same algebra is used to make inferences of either kind. Interestingly, Bayes actually states his question in a way that might make the idea of assigning a probability distribution to a parameter palatable to a frequentist. He supposes that a billiard ball is thrown at random onto a billiard table, and that the probabilities p and q are the probabilities that subsequent billiard balls will fall above or below the first ball. By making the binomial parameter depend on a random event, he cleverly escapes a philosophical quagmire that was an issue he most likely was not even aware of.
Read more about this topic: Bayesian Inference
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