Distribution

Distribution

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If X∼N(0,1)X \sim N(0,1), then Z=X2∼X2(1)\mathbb{Z} = X^2 \sim \mathcal{X}^2(1)

Beta(α\alpha, β\beta) 0<x<10 < x < 1

Uniform (α\alpha, β\beta) a<x<ba < x < b

Gamma γ(α,β)\gamma(\alpha, \beta)

f(x)=xα−1Γ(α)θαe−xθf(x) = {x^{\alpha - 1} \over \Gamma(\alpha) \theta^\alpha} e^{-x \over \theta}

Exponential(θ\theta) = γ(α=1,theta)\gamma(\alpha = 1, theta)

Chi-squared(γ\gamma) = γ(α=γ2,θ=2)\gamma(\alpha = {\gamma \over 2}, \theta = 2)

X∼N(μ,σx2)\mathcal{X} \sim N(\mu, \sigma_x^2) → 12πσe−(x−μ)22σx2{1 \over \sqrt{2 \pi} \sigma} e^{-{(x-\mu)^2} \over {2\sigma_x^2}}

Y=g(x)Y=g(x)

fy(y)=∑xkfx(xk)∣dxdy∣@x=xkf_y(y) = \sum\limits_{x_k} f_x(x_k) |{dx \over dy}|_{\text{@} x = x_k}

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