z-logo
Premium
An alternate development of conditioning
Author(s) -
Winter B. B.
Publication year - 1979
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.1979.tb00675.x
Subject(s) - uniqueness , mathematics , conditioning , conditional probability distribution , probability measure , function (biology) , conditional probability , conditional expectation , combinatorics , regular conditional probability , measure (data warehouse) , value (mathematics) , random variable , discrete mathematics , probability distribution , mathematical analysis , statistics , computer science , probability mass function , data mining , evolutionary biology , biology
  The “classical” development of conditioning, due to K olmogorov , does not agree with the “practical” (more intuitive, but unrigorous) way in which probabilists and statisticians actually think about conditioning. This paper describes an alternative to the classical development. It is shown that standard concepts and results can be developed, rigorously, along lines, which correspond to the “practical” approach, and so as to include the classical material as a special case. More specifically, let Xand Y be random variables (r.v.‘s) from (Ω, f, P) to ( x, f x ) and (y. f y .), respectively. In this paper, the fundamental concept is the conditional probability P(AX = x ), a function of xε x which satisfies a “natural” defining condition. This is used to define a conditional distribution P y/x , as a mapping x × f y ‐R such that, as a function of B, P ylx=x ,(B ) is a probability measure on f y . Then, for a numerical r.v. Y , conditional expectation E(Y/X) is defined as a mapping x →r̄ whose value at x isE(Y/X = x) = ydP Y/x=i (y ). Basic properties of conditional probabilities, distributions, and expectations, are derived and their existence and uniqueness are discussed. Finally, for a sub‐o‐algebra and a numerical r.v. Y , the classical conditional expectation E(Y) is obtained as E(Y/X) with X = i , the identity mapping from (Ω, f) to (Ω).

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here