Thomas Bayes (/beɪz/; c. 1701 – 7 April 1761)[note 1] was an English statistician, philosopher and Presbyterian minister who is known for formulating a specific case of the theorem that bears his name: Bayes' theorem. Bayes never published what would become his most famous accomplishment; his notes were edited and published after his death by Richard Price.
He is known to have published two works in his lifetime, one theological and one mathematical:
Divine Benevolence, or an Attempt to Prove That the Principal End of the Divine Providence and Government is the Happiness of His Creatures (1731)
An Introduction to the Doctrine of Fluxions, and a Defence of the Mathematicians Against the Objections of the Author of The Analyst (published anonymously in 1736), in which he defended the logical foundation of Isaac Newton's calculus ("fluxions") against the criticism of George Berkeley, author of The Analyst
It is speculated that Bayes was elected as a Fellow of the Royal Society in 1742 on the strength of the Introduction to the Doctrine of Fluxions, as he is not known to have published any other mathematical works during his lifetime.
Bayes's solution to a problem of inverse probability was presented in "An Essay towards solving a Problem in the Doctrine of Chances" which was read to the Royal Society in 1763 after Bayes's death. Richard Price shepherded the work through this presentation and its publication in the Philosophical Transactions of the Royal Society of London the following year. This was an argument for using a uniform prior distribution for a binomial parameter and not merely a general postulate. This essay gives the following theorem (stated here in present-day terminology).
Suppose a quantity R is uniformly distributed between 0 and 1. Suppose each of X1, ..., Xn is equal to either 1 or 0 and the conditional probability that any of them is equal to 1, given the value of R, is R. Suppose they are conditionally independent given the value of R. Then the conditional probability distribution of R, given the values of X1, ..., Xn, is
In the first decades of the eighteenth century, many problems concerning the probability of certain events, given specified conditions, were solved. For example: given a specified number of white and black balls in an urn, what is the probability of drawing a black ball? Or the converse: given that one or more balls has been drawn, what can be said about the number of white and black balls in the urn? These are sometimes called "inverse probability" problems.
Bayesian probability is the name given to several related interpretations of probability as an amount of epistemic confidence – the strength of beliefs, hypotheses etc. – rather than a frequency. This allows the application of probability to all sorts of propositions rather than just ones that come with a reference class. "Bayesian" has been used in this sense since about 1950. Since its rebirth in the 1950s, advancements in computing technology have allowed scientists from many disciplines to pair traditional Bayesian statistics with random walk techniques. The use of the Bayes theorem has been extended in science and in other fields.
Bayes himself might not have embraced the broad interpretation now called Bayesian, which was in fact pioneered and popularised by Pierre-Simon Laplace; it is difficult to assess Bayes's philosophical views on probability, since his essay does not go into questions of interpretation. There, Bayes defines probability of an event as (Definition 5) "the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon its happening". Within modern utility theory, the same definition would result by rearranging the definition of expected utility (the probability of an event times the payoff received in case of that event – including the special cases of buying risk for small amounts or buying security for big amounts) to solve for the probability. As Stigler points out, this is a subjective definition, and does not require repeated events; however, it does require that the event in question be observable, for otherwise it could never be said to have "happened". Stigler argues that Bayes intended his results in a more limited way than modern Bayesians. Given Bayes's definition of probability, his result concerning the parameter of a binomial distribution makes sense only to the extent that one can bet on its observable consequences.
^Bayes's tombstone says he died at 59 years of age on 7 April 1761, so he was born in either 1701 or 1702. Some sources erroneously write the death date as 17 April, but these sources all seem to stem from a clerical error duplicated; no evidence argues in favour of a 17 April death date. Bayes's birth date is unknown, likely due to the fact he was baptised in a Dissenting church, which either did not keep or was unable to preserve its baptismal records; accordRoyal Society Library and Archive catalogue,
Thomas Bayes (1701–1761)
^Terence O'Donnell, History of Life Insurance in Its Formative Years (Chicago: American Conservation Co:, 1936), p. 335 (caption "Rev. T. Bayes: Improver of the Columnar Method developed by Barrett.")
F. Thomas Bruss (2013), "250 years of 'An Essay towards solving a Problem in the Doctrine of Chance. By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S.' ", doi:10.1365/s13291-013-0077-z, Jahresbericht der Deutschen Mathematiker-Vereinigung, Springer Verlag, Vol. 115, Issue 3–4 (2013), 129–133.
Dale, Andrew I. (2003.) "Most Honourable Remembrance: The Life and Work of Thomas Bayes". ISBN0-387-00499-8. Springer, 2003.
____________. "An essay towards solving a problem in the doctrine of chances" in Grattan-Guinness, I., ed., Landmark Writings in Western Mathematics. Elsevier: 199–207. (2005).
McGrayne, Sharon Bertsch. (2011). The Theory That Would Not Die: How Bayes's Rule Cracked The Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of Controversy. New Haven: Yale University Press. ISBN9780300169690OCLC 670481486