FIND ME ON

GitHub

LinkedIn

Uniform Random Variable

🌱

Probability

Let Ω=[0,1],J={all intervals in [0,1]}\Omega=[0,1],\mathcal{J}=\{ \text{all intervals in }[0,1] \}. For an interval IJI\in \mathcal{J}, set P(I)=length of I\mathbb{P}(I)=\text{length of }I. Given this setup, there is a σ-algebra M\mathcal{M} on [0,1][0,1] with probability measure λ\lambda such that JM\mathcal{J}\subseteq \mathcal{M} and λ(I)=length of I\lambda(I)=\text{length of }I.

There exists a probability space (Ω,M,P)(\Omega,\mathcal{M},\mathbb{P}^{*}) such that Ω=[0,1],MJ\Omega=[0,1],\mathcal{M}\supseteq\mathcal{J} and for any interval I[0,1]I\subseteq[0,1], P(I)=length of I\mathbb{P}^{*}(I)=\text{length of }I. This triple is called the uniform distribution on [0,1][0,1] or the Lebesgue measure on [0,1][0,1].

A RV XX is called “uniformly distributed over the interval (a,b)(a,b)”or “uniform over (a,b)(a,b)” if its pdf is f(x)=\left\{\begin{array} 1\frac{1}{b-a} & a<x<b\\ 0 & otherwise \end{array}\right. and cdf F(x)=\left\{ \begin{array} 1 0&x\le a\\ \frac{x-a}{b-a}&a<x<b\\ 1&x\ge b \end{array} \right. For RV X\mboxUniform(a,b)X\sim \mbox{Uniform}(a,b) E[X]=a+b2\mboxVar(X)=(ba)212\begin{align*} E[X]&=\frac{a+b}{2}\\ \mbox{Var}(X)&=\frac{(b-a)^{2}}{12} \end{align*}

Linked from