Discrete Random Variable

From GM-RKB
(Redirected from discrete random variable)
Jump to: navigation, search

A discrete random variable is a random variable associated with a countable sample space.



References

2006

  • (Dubnicka, 2006c) ⇒ Suzanne R. Dubnicka. (2006). “Random Variables - STAT 510: Handout 3." Kansas State University, Introduction to Probability and Statistics I, STAT 510 - Fall 2006.
    • MATHEMATICAL DEFINITION: A random variable [math]X[/math] is a function whose domain is the sample space S and whose range is the set of real numbers [math]R[/math] = {x : −∞ < [math]x[/math] < ∞.}. Thus, a random is obtained by assigning a numerical value to each outcome of a particular experiment.
    • WORKING DEFINITION: A random variable is a variable whose observed value is determined by chance.
    • NOTATION: We denote a random variable [math]X[/math] with a capital letter; we denote an observed value of [math]X[/math] as [math]x[/math], a lowercase letter.
    • TERMINOLOGY : The support of a random variable [math]X[/math] is set of all possible values that [math]X[/math] can assume. We will often denote the support set as SX. If the random variable [math]X[/math] has a support set SX that is either finite or countable, we call [math]X[/math] a 'discrete random variable.

1986

  • (Larsen & Marx, 1986) ⇒ Richard J. Larsen, and Morris L. Marx. (1986). “An Introduction to Mathematical Statistics and Its Applications, 2nd edition." Prentice Hall
    • 'Definition 3.2.1. A real-valued function whose domain is the sample space S is called a random variable. We denote random variables by uppercase letters, often [math]X[/math], Y, or Z.
    • If the range of the mapping contains either a finite or countably infinite number of values, the random variable is said to be discrete ; if the range includes an interval of real numbers, bounded or unbounded, the random variable is said to be continuous.
    • Associated with each discrete random variable [math]Y[/math] is a probability density function (or pdf). “fY(y). By definition, fY(y) is the sum of all the probabilities associated with outcomes in [math]S[/math] that get mapped into [math]y[/math] by the random variable Y. That is.
      • fY(y) = P({s(∈)S |Y(s) = y})
    • Conceptually, fY(y) describes the probability structure induced on the real line by the random variable Y.
    • For notational simplicity, we will delete all references to [math]s[/math] and [math]S[/math] and write: fY(y) = P(Y(s)=y). In other words, fY(y) is the "probability that the random variable Y takes on the value y."