Theories which analyze probability in terms of beliefs or attitudes rather than anything in the world itself.

For one theory, associated mainly with Bruno De Finetti (1906-1985), the degree of probability of something is the degree of the speaker’s belief, measured by his betting behavior, but subject to the constraint that his bets must be ‘coherent’; that is, he must not bet in such a way as to lose whatever happens (sometimes called ‘having a Dutch book made against one’).

This constraint still leaves probabilities dependent on the vagaries of individual attitudes, unless we substitute those of ‘the rational man’ – but that takes us away from subjectivism. Others, notably Stephen Edelston Toulmin (1922-), offer a speech act theory whereby to call something probable is to assert it, though only tentatively.

This may well apply to some uses of ‘probably’, but hardly to all, and shares the objections to other speech act theories.

Source:

H E Kyburg, Probability and Inductive Logic (1970), ch. 6

The word probability has been used in a variety of ways since it was first applied to the mathematical study of games of chance. Does probability measure the real, physical tendency of something to occur or is it a measure of how strongly one believes it will occur, or does it draw on both these elements? In answering such questions, mathematicians interpret the probability values of probability theory.

There are two broad categories^{[1]}^{[2]} of **probability interpretations** which can be called “physical” and “evidential” probabilities. Physical probabilities, which are also called objective or frequency probabilities, are associated with random physical systems such as roulette wheels, rolling dice and radioactive atoms. In such systems, a given type of event (such as a die yielding a six) tends to occur at a persistent rate, or “relative frequency”, in a long run of trials. Physical probabilities either explain, or are invoked to explain, these stable frequencies. The two main kinds of theory of physical probability are frequentist accounts (such as those of Venn,^{[3]} Reichenbach^{[4]} and von Mises^{[5]}) and propensity accounts (such as those of Popper, Miller, Giere and Fetzer).^{[6]}

Evidential probability, also called Bayesian probability, can be assigned to any statement whatsoever, even when no random process is involved, as a way to represent its subjective plausibility, or the degree to which the statement is supported by the available evidence. On most accounts, evidential probabilities are considered to be degrees of belief, defined in terms of dispositions to gamble at certain odds. The four main evidential interpretations are the classical (e.g. Laplace’s)^{[7]} interpretation, the subjective interpretation (de Finetti^{[8]} and Savage^{[9]}), the epistemic or inductive interpretation (Ramsey,^{[10]} Cox^{[11]}) and the logical interpretation (Keynes^{[12]} and Carnap^{[13]}). There are also evidential interpretations of probability covering groups, which are often labelled as ‘intersubjective’ (proposed by Gillies^{[14]} and Rowbottom^{[6]}).

Some interpretations of probability are associated with approaches to statistical inference, including theories of estimation and hypothesis testing. The physical interpretation, for example, is taken by followers of “frequentist” statistical methods, such as Ronald Fisher^{[dubious – discuss]}, Jerzy Neyman and Egon Pearson. Statisticians of the opposing Bayesian school typically accept the existence and importance of physical probabilities, but also consider the calculation of evidential probabilities to be both valid and necessary in statistics. This article, however, focuses on the interpretations of probability rather than theories of statistical inference.

The terminology of this topic is rather confusing, in part because probabilities are studied within a variety of academic fields. The word “frequentist” is especially tricky. To philosophers it refers to a particular theory of physical probability, one that has more or less been abandoned. To scientists, on the other hand, “frequentist probability” is just another name for physical (or objective) probability. Those who promote Bayesian inference view “frequentist statistics” as an approach to statistical inference that recognises only physical probabilities. Also the word “objective”, as applied to probability, sometimes means exactly what “physical” means here, but is also used of evidential probabilities that are fixed by rational constraints, such as logical and epistemic probabilities.

It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics, there has seldom been such complete disagreement and breakdown of communication since the Tower of Babel. Doubtless, much of the disagreement is merely terminological and would disappear under sufficiently sharp analysis.

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