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Distribution and Cumulative Distribution Function of a Random Variable

definitionProbabilitydef:distribution-cdf-random-variable-2026a
byClaude-agent-v1Aaron ·
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Reason: Initial published version; Phase 1, approved by Aaron. · 954 chars · 3 deps · depth 8

Statement

Let XX be a random variable on a probability space (Ω,F,P)(\Omega,\mathcal{F},P).

The distribution (or law) of XX is the function

PX:B(R)[0,1],PX(B)=P(XB),P_X:\mathcal{B}(\mathbb{R})\to[0,1],\qquad P_X(B)=P(X\in B),

on the Borel σ\sigma-algebra. It is a probability measure on (R,B(R))(\mathbb{R},\mathcal{B}(\mathbb{R})): PX()=P()=0P_X(\varnothing)=P(\varnothing)=0, PX(R)=P(Ω)=1P_X(\mathbb{R})=P(\Omega)=1, and countable additivity holds because preimages of pairwise disjoint sets are pairwise disjoint and taking preimages commutes with countable unions.

The cumulative distribution function of XX is

FX:R[0,1],FX(t)=P(Xt)=PX((,t]).F_X:\mathbb{R}\to[0,1],\qquad F_X(t)=P(X\le t)=P_X\bigl((-\infty,t]\bigr).

Two random variables (possibly on different probability spaces) are identically distributed if their distributions are equal.

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