Formulas
Probability Density Functions (PDFs)
- Standard Normal PDF\[f(x)= {e^{-x^2} \over \sqrt{2 \pi}}\]
- General Normal PDF\[f(x)= {e^{{-(x-\mu)^2} \over 2\sigma^2} \over \sqrt{2 \pi\sigma^2}}\]
- Chi-Square PDF\[\frac{(1/2)^{k/2}}{\Gamma(k/2)} x^{k/2 - 1} e^{-x/2}\,\]
- Gamma PDF\[x^{k-1} \frac{\exp{\left(-x/\theta\right)}}{\Gamma(k)\,\theta^k}\,\!\]
- Beta PDF\[ \frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}\, x^{\alpha-1}(1-x)^{\beta-1}\!\]
- Student's T PDF\[\frac{\Gamma(\frac{\nu+1}{2})} {\sqrt{\nu\pi}\,\Gamma(\frac{\nu}{2})} \left(1+\frac{x^2}{\nu} \right)^{-(\frac{\nu+1}{2})}\!\]
- Poisson PDF\[\frac{e^{-\lambda} \lambda^k}{k!}\!\]
- Chi PDF\[\frac{2^{1-k/2}x^{k-1}e^{-x^2/2}}{\Gamma(k/2)}\]
- Cauchy PDF\[\frac{1}{\pi\gamma \left[1 + \left(\frac{x-x_0}{\gamma}\right)^2\right]}\]
- Exponential PDF\[ \lambda e^{-\lambda x},\; x \ge 0\]
- F Distribution PDF\[ \frac {(\frac {d_1 x}{d_1 x + d_2})^{ d_1/2} ( 1 - \frac {d_1 x} {d_1 x + d2}) ^ {d_2/2}} { xB(d_1/2 , d_2/2) } \]
- Bernoulli PMF\[ f(k;p) \begin{cases} \mbox{p if k = 1,} \\ \mbox{1 - p if k = 0,} \\ \mbox{0 otherwise} \end{cases} \]
- Binomial PMF\[ \begin{pmatrix} n \\ k \end{pmatrix} p^k (1-p)^{n-k}\]
- Negative Binomial PMF\[ \begin{pmatrix} k + r - 1 \\ k \end{pmatrix} p^r(1-p)^k \]
- Geometric PMF\[ \begin{pmatrix} 1-p \end{pmatrix} ^{k-1}p \]
- Erlang PDF\[ \frac {\lambda x^{k-1}e^{-\lambda x}} {(k-1)!} \]
- Laplace PDF\[ \frac {1}{2b} \exp (- \frac{|x-\mu|}{b}) \]
- Continuous Uniform PDF\[ f(x) = \begin{cases} \frac{1}{b-a} \mbox{ for } a \le x \le b \\ 0 \mbox{ for } x < a \mbox{ or } x > b \end{cases} \]
- Discrete Uniform PMF\[ f(x) = \begin{cases} 1/n \mbox{ for } a \le x \le b, \\ 0 \mbox{ otherwise} \end{cases} \]
- Logarithmic PDF\[ f(k) = \frac{-1}{ln(1-p)} \frac{p^k}{k} \]
- Logistic PDF\[ f(x;u,s) = \frac{e^{-(x-\mu)/s}} {s(1+e^{-(x-\mu)/s})^2} \]
- Logistic-Exponential PDF\[ f(x;\beta) = \frac { \beta e^x(e^x - 1)^{\beta-1}} {(1+(e^x-1)^\beta))^2} \mbox{ }\mbox{ }x, \beta > 0 \]
- Power Function PDF\[ f(x) = \frac {\alpha(x-a)^{\alpha-1}} {(b-a)^\alpha} \]
- Benford's Law\[ P(d) = \log_b(d + 1)- \log_b(d) = \log_b(\frac{d + 1}{d}) \]
- Pareto PDF\[ \frac {kx^k_m} {x^{k+1}} \]
- Non-Central Student T PDF\[ f(t)=\frac{\nu^{\nu/2}e^{-\nu\mu^2/2(t^2+\nu)}} {\sqrt{\pi}\Gamma(\nu/2)2^{(\nu-1)/2}(t^2+\nu)^{(\nu+1)/2}} \times\int\limits_0^\infty x^\nu\exp\left[-\frac{1}{2}\left(x-\frac{\mu t}{\sqrt{t^2+\nu}}\right)^2\right]dx \]
- ArcSine PDF\[ f(x) = \frac{1}{\pi \sqrt{x(1-x)}} \]
- Circle PDF\[ f(x)={2\sqrt{r^2 - x^2}\over \pi r^2 }, \forall x \in [-r , r] \]
- U-Quadratic PDF\[\alpha \left ( x - \beta \right )^2 \]
Transformations
- Standard Normal to General Normal Transformation\[\mu+\sigma\times X\]
- General Normal to Standard Normal Transformation\[X-\mu \over \sigma\]
- Standard Normal to Chi Transformation\[|\ X |\]
- Standard Normal to Chi-Square Transformation\[\sum_{k=1}^{\nu} X_k^2\]
- Gamma to General Normal Transformation\[\mu=\alpha\times\beta;\sigma^2=\alpha^2\times\beta;\beta\longrightarrow\infty\]
- Gamma to Exponential Transformation: The special case of \({\Gamma}(k=1, \theta=1/\lambda)\,\) is equivalent to exponential \(Exp(\lambda)\).
- Gamma to Beta Transformation\[X_1 \over X_1 + X_2\].
- Student's T to Standard Normal Transformation\[n\longrightarrow\infty\]
- Student's T to Cauchy Transformation\[n=1 \ \]
- Cauchy to General Cauchy Transformation\[a + \alpha\times X\]
- General Cauchy to Cauchy Transformation\[a=0; \alpha=1 \ \]
- Fisher's F to Student's T\[\sqrt X \]
- Student's T to Fisher's F\[ X^2 \]
- Bernoulli to Binomial Transformation\[ \sum X_i \] (iid)
- Binomial to Bernoulli Transformation\[\begin{pmatrix} n = 1 \end{pmatrix}\]
- Binomial to General Normal Transformation\[ \begin{vmatrix} \mu = np \\ \sigma^2 = np(1-p) \\n \rightarrow \infty \end{vmatrix} \]
- Binomial to Poisson Transformation\[ \begin{vmatrix}\mu = np \\ n \rightarrow \infty \end{vmatrix} \]
- Negative Binomial to Geometric Transformation\[ \begin{pmatrix} r = 1 \end{pmatrix} \]
- Erlang to Exponential Transformation\[ \begin{pmatrix} k = 1 \end{pmatrix} \]
- Erlang to Chi-Square Transformation\[ \begin{pmatrix} \alpha = 2 \end{pmatrix} \]
- Laplace to Exponential Transformation\[\begin{pmatrix} \begin{vmatrix} X \end{vmatrix} \\ \alpha_1 = \alpha_2 \end{pmatrix}\]
- Exponential to Laplace Transformation\[ x_1 - x_2 \ \]
- Beta to Arcsine Transformation\[ \alpha = \beta = \frac{1}{2} \]
- Noncentral Student's T to Normal\[ Z=\lim_{\nu\to\infty}T \]
- Noncentral Student's T to Student's T\[ \mu = 0 \]
- Standard Uniform to Pareto\[ \lambda X ^{-1/K} \]
- Standard Uniform to Benford\[ 10^X \]
- Pareto to Exponential\[ log(X/\lambda) \]
- Logistic Exponential to Exponential\[ \beta = 1 \]
- Zipf to Discrete Uniform\[ a = 0, a = 1, b = n \ \]
- Discrete Uniform to Rectangular\[ a = 0, b = n \ \]
- SOCR Home page: http://www.socr.ucla.edu
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