Richard Carl Jeffrey (August 5, 1926 – November 9, 2002) was an American philosopher, logician, and probability theorist. He is best known for developing and championing the philosophy of radical probabilism and the associated heuristic of probability kinematics, also known as Jeffrey conditioning. Life and career Born in Boston, Massachusetts, Jeffrey served in the U.S. Navy during World War II. As a graduate student he studied under Rudolf Carnap and Carl Hempel. He received his M.A. from the University of Chicago in 1952 and his Ph.D. from Princeton in 1957. After holding academic positions at MIT, City College of New York, Stanford University, and the University of Pennsylvania, he joined the faculty of Princeton in 1974 and became a professor emeritus there in 1999. He was also a visiting professor at the University of California, Irvine. Jeffrey, who died of lung cancer at the age of 76, was known for his sense of humor, which often came through in his breezy writing style. In the preface of his posthumously published Subjective Probability, he refers to himself as "a fond foolish old fart dying of a surfeit of Pall Malls". Philosophical work As a philosopher, Jeffrey specialized in epistemology and decision theory. He is perhaps best known for defending and developing the Bayesian approach to probability. Jeffrey also wrote, or co-wrote, two widely used and influential logic textbooks: Formal Logic: Its Scope and Limits, a basic introduction to logic, and Computability and Logic, a more advanced text dealing with, among other things, the famous negative results of twentieth-century logic such as Gödel's incompleteness theorems and Tarski's indefinability theorem. Radical probabilism In frequentist statistics, Bayes' theorem provides a useful rule for updating a probability when new frequency data becomes available. In Bayesian statistics, the theorem itself plays a more limited role. Bayes' theorem connects probabilities that are held simultaneously. It does not tell the learner how to update probabilities when new evidence becomes available over time. This subtlety was first pointed out in terms by Ian Hacking in 1967. However, adapting Bayes' theorem, and adopting it as a rule of updating, is a temptation. Suppose that a learner forms probabilities Pold(A&B)=p and Pold(B)=q. If the learner subsequently learns that B is true, nothing in the axioms of probability or the results derived therefrom tells him how to behave. He might be tempted to adopt Bayes' theorem by analogy and set his Pnew(A)