User:GFauxPas/Sandbox

Welcome to my sandbox, you are free to play here as long as you don't track sand onto the main wiki. --GFauxPas 09:28, 7 November 2011 (CST)

Symbols:LaTeX Commands/ProofWiki Specific





Theorem 1 : Characterization of Probability Density Function
Let $\struct {\Omega, \Sigma, \Pr}$ be a probability space.

Let $X: \Omega \to \R$ be a continuous random variable on $\struct {\Omega, \Sigma, \Pr}$.

Let $\Omega_X = \Img X$, the image of $X$.

Let the probability density function of $X$ is the mapping $f_X: \R \to \closedint 0 1$ be defined as:


 * $\forall x \in \R: \map {f_X} x = \begin {cases}

\displaystyle \lim_{\epsilon \mathop \to 0^+} \frac {\map \Pr {x - \frac \epsilon 2 \le X \le x + \frac \epsilon 2} } \epsilon & : x \in \Omega_X \\ 0 & : x \notin \Omega_X \end {cases}$

Suppose that the cumulative distribution function of $X$ defines a continuously differentiable real function $F_X: x \mapsto \map \Pr {X \le x}$.

Then the probability density function of $X$ is the mapping $f_X: \R \to \closedint 0 1$ satisfies:


 * $\dfrac{\d}{\d x} \map{F_X}{x} = \map {f_X} x$.

Theorem 2: Cumulative Distribution Function as Improper Riemann Integral

 * $\displaystyle \map{F_X}x = \int_{\to -\infty}^x \map {f_X}{t} \rd t$

under the same hypotheses