An Essay towards solving a Problem in the Doctrine of Chances is a work on the mathematical theory of probability by the Reverend Thomas Bayes, published in 1763,[1] two years after its author's death. It included a statement of a special case of what is now called Bayes' theorem. In 18th-century English, the phrase "doctrine of chances" meant the theory of probability. It had been introduced as the title of a book by Abraham de Moivre.
Bayes supposed a sequence of independent experiments, each having as its outcome either success or failure, the probability of success being some number p between 0 and 1. But then he supposed p to be an uncertain quantity, whose probability of being in any interval between 0 and 1 is the length of the interval. In modern terms, p would be considered a random variable uniformly distributed between 0 and 1. Conditionally on the value of p, the trials resulting in success or failure are independent, but unconditionally (or "marginally") they are not. That is because if a large number of successes are observed, then p is more likely to be large, so that success on the next trial is more probable. The question Bayes addressed was: what is the conditional probability distribution of p, given the numbers of successes and failures so far observed. The answer is that its probability density function is

(and ƒ(p) = 0 for p < 0 or p > 1) where k is the number of successes so far observed, and n is the number of trials so far observed. This is what today is called the Beta distribution with parameters k + 1 and n − k + 1.
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Bayes' preliminary results (Propositions 3, 4, and 5) imply the truth of the theorem that is named for him. Particularly, Proposition 5 gives a simple description of conditional probability:
However, it does not appear that Bayes emphasized or focused on this finding. He presented his work as the solution to a problem:
Bayes gave an example of a man trying to guess the ratio of "blanks" and "prizes" at a lottery. So far the man has watched the lottery draw ten blanks and one prize. Given these data, Bayes showed in detail how to compute the probability that the ratio of blanks to prizes is between 9:1 and 11:1 (the probability is low - about 7.7%). He went on to describe that computation after the man has watched the lottery draw twenty blanks and two prizes, forty blanks and four prizes, and so on. Finally, having drawn 10,000 blanks and 1,000 prizes, the probability reaches about 97%.[2]
Bayes' main result (Proposition 9) is the following in modern terms:
. After observing
successes and
failures,
It is unclear whether Bayes was a "Bayesian" in the modern sense. That is, whether he was interested in Bayesian inference, or merely in probability. Proposition 9 seems "Bayesian" in its presentation as a probability about the parameter
. However, Bayes stated his question in a manner that suggests a frequentist viewpoint: he supposed that a billiard ball is thrown at random onto a billiard table, and considered further billiard balls that fall above or below the first ball with probabilities
and
. The algebra is of course identical no matter which view is taken.
Richard Price discovered Bayes' essay and its now-famous theorem in Bayes' papers after Bayes' death. He believed that Bayes' Theorem helped prove the existence of God ("the Deity") and wrote the following in his introduction to the essay:
In modern terms this is an instance of the teleological argument.
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