|Probability distribution: A probability distribution is the distribution of the probabilities for discrete individual events in a given experimental setup or situation. It is given by a probability function. While a uniform distribution is to be expected for a dice, the distribution of e.g. the expected body sizes of a human population will be concentrated around a mean value. See also probability function._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. |
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Nonfactualism/NF/Field: must assume that acceptance of conditionals is not regulated by the normal probability law, which governs the acceptance of "fact sentences".
Linearity Principle/Lewis/Field: P(D) = P (D I B). P(B) + P(D I ~ B). P(~ B) - is not acceptable for the nonfactualism if D is a form of A > C. - That is, the law for belief degree fails.
Belief degree/conditional: in conditional conditions, the classical probability laws for belief degrees do not apply. - Disquotational truth/conditional: refers to the whole: "when Clinton dies Gore becomes President" is true iff Clinton dies and Gore becomes president.
Non-disquotational: with simple sentences such as disquotational truth.
For conditionals: simplest solution: without truth value._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution.
Realism, Mathematics and Modality Oxford New York 1989
Truth and the Absence of Fact Oxford New York 2001
Science without numbers Princeton New Jersey 1980