Formulas

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Current revision as of 12:57, 18 January 2011

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: $\begin{pmatrix} n \\ k \end{pmatrix} p^k (1-p)^{n-k}$
Multinomial PMF: $f(x_1, x_2, \cdots, x_k)={n\choose x_1,x_2,\cdots, x_k}p_1^{x_1}p_2^{x_2}\cdots p_k^{x_k}$ , where $x_1+x_2+\cdots+x_k=n$ , $p_1+p_2+\cdots+p_k=1$ , and $0 \le x_i \le n, 0 \le p_i \le 1$ .
Negative Binomial PMF: $\begin{pmatrix} k + r - 1 \\ k \end{pmatrix} p^r(1-p)^k$
Negative-Multinomial Binomial PMF: $P(k_o, \cdots, k_r) = \Gamma(k_o + \sum_{i=1}^r{k_i}) \frac{p_o^{k_o}}{\Gamma(k_o)} \prod_{i=1}^r{\frac{p_i^{k_i}}{k_i!}}$
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: $\alpha \left ( x - \beta \right )^2$
Standard Uniform PDF: $U(0,1) = f(x) = \begin{cases} {1} \mbox{ for } 0 \le x \le 1 \\ 0 \mbox{ for } x < 0 \mbox{ or } x > 1 \end{cases}$
Zipf: $\frac{1/(k+q)^s}{H_{N,s}}$
Inverse Gamma: $\frac{\beta^\alpha}{\Gamma(\alpha)} x^{-\alpha - 1} \exp \left(\frac{-\beta}{x}\right)$
Fisher-Tippett: $\frac{z\,e^{-z}}{\beta}\!$
where $z = e^{-\frac{x-\mu}{\beta}}\!$
Gumbel: $f(x) = e^{-x} e^{-e^{-x}}.$
HyperGeometric: ${{{m \choose k} {{N-m} \choose {n-k}}}\over {N \choose n}}$
Log-Normal: $\frac{1}{x\sigma\sqrt{2\pi}}\exp\left[-\frac{\left(\ln(x)-\mu\right)^2}{2\sigma^2}\right]$
Gilbrats: $\frac{1}{\sigma\sqrt{2\pi}}\exp\left[-\frac{\left(\ln(x)\right)^2}{2\sigma^2}\right]$
Hyperbolic Secant: $\frac12 \; \operatorname{sech}\!\left(\frac{\pi}{2}\,x\right)\!$
Gompertz: $b e^{-bx} e^{-\eta e^{-bx}}\left[1 + \eta\left(1 - e^{-bx}\right)\right]$
Standard Cauchy: $f(x; 0,1) = \frac{1}{\pi (1 + x^2)}. \!$
Rectangular: $f(x)=\frac{1}{n+1}.(x=0,1,...,n)\!$
Beta-Binomial: $f(x)=\frac{\Gamma(x+a)\Gamma(n-x+b)\Gamma(a+b)\Gamma(n+2)}{(n+1)\Gamma(a+b+n)\Gamma(a)\Gamma(b)\Gamma(x+1)\Gamma(n-x+1)}.(x=0,1,...,n)\!$
Negative Hypergeometric: $f(x)=\frac{\begin{pmatrix} n_1+x-1 \\ x \end{pmatrix} \begin{pmatrix} n_3-n_1+n_2-x-1 \\ n_2-x \end{pmatrix}}{\begin{pmatrix} n_3+n_2-1 \\ n_2 \end{pmatrix}}. (x=max(0,n_1+n_2-n_3),...,n_2)\!$
Standard Power: $f(x; \beta) = \beta x^{\beta - 1} \!$
Power_Series: $f(x; c; A(c)) = a(x) c^x / A(c). (x=(0,1,...), c>0, A(c)=\sum_{x}a(x) c^x) \!$
Zeta: $f(x)=\frac{1}{x^a \sum_{i=1}^{\infty}(\frac{1}{i})^a}. (x=1,2,...) \!$
Logarithm: $f(x)=\frac{-(1-c)^x}{x\log c}. (x=1,2,..., 0<c<1) \!$
Beta_Pascal: $f(x; a, b, n) = \binom{n-1+x}{x} \frac{B(n+a, b+x)}{B(a,b)}. (x=(0,1,...); a+b=n) \!$
Gamma_Poisson: $f(x; \alpha, \beta) = \frac{\Gamma(x+\beta) \alpha^x}{\Gamma(\beta) (1+\alpha)^{\beta+x} x!}.(x=(0,1,...); \alpha>0; \beta>0) \!$
Pascal: $f(x; p, n) = \binom{n-1+x}{x} p^n (1-p)^x. (x=(0,1,...,n); 0 \leq p \leq 1)\!$
Polya: $f(x; n, p, \beta) = \binom{n}{x} \frac{\prod_{j=0}^{x-1}(p+j\beta) \prod_{k=0}^{n-x-1}(1-p+k\beta)}{\prod_{i=0}^{n-1}(1+i\beta)}. (x=\{0,1,...,n\}) \!$
Normal-Gamma: $f(x, \tau; \mu, \lambda,\alpha,\beta) = \frac{\beta^\alpha \sqrt(\lambda)}{\Gamma(\alpha) \sqrt(2 \pi)} \tau^{\alpha-1/2} exp(-\beta \tau) exp(-\frac{\lambda \tau (x-\mu)^2}{2}).(\tau>0) \!$
Discrete_Weibull: $f(x; p, \beta) = (1-p)^{x^\beta}-(1-p)^{(x+1)^\beta}. (x=\{0,1,...\}) \!$
Log Gamma: $f(x)=[1/ \alpha^\beta \Gamma(\beta)]e^{\beta x}e^{-e^x/a}. (-\infty<x<\infty) \!$
Generalized Gamma: $f(x)=\frac{\gamma}{\alpha^{\gamma \beta}\Gamma(\beta)}x^{\gamma \beta-1}e^{-(x/\alpha)^\gamma}. (x>0) \!$
Noncentral-Beta: $f(x; \beta, \gamma, \delta) = \sum_{i=0}^{\infty}\frac{\Gamma(i+\beta+\gamma)}{\Gamma(\gamma) \Gamma(i+\beta)} \frac{exp(-\delta/2)}{i!} (\delta/2)^i x^{i+\beta-1} (1-x)^{\gamma-1}. (0 \leq x \leq 1). \!$
Inverse Gausian: $f(x)=\sqrt{\frac{\lambda}{2\pi x^3}}e^{-\frac{\lambda}{2\mu^2 x}(x-\mu)^2}. (x>0) \!$
Noncentral_chi-square: $f(x; n,\delta) = f(x; n,\delta) = \sum_{k=0}^{\infty}\frac{exp(-\delta/2) (\delta/2)^k}{k!}\frac{exp(-x/2) x^{(n+2k)/2-1}}{2^{(n+2k)/2} \Gamma(\frac{n+2k}{2})}. \!$
Standard Wald: $f(x)=\sqrt{\frac{\lambda}{2\pi x^3}}e^{-\frac{\lambda}{2x}(x-1)^2}. (x>0) \!$
Inverted Beta: $f(x)=\frac{x^{\beta-1}(1+x)^{-\beta-\gamma}}{B(\beta,\gamma)}. (x>0, \beta>1, \gamma>1) \!$
Arctangent: $f(x; \lambda, \phi)= \frac{\lambda}{[arctan(\lambda \phi)+\pi/2][1+\lambda^2 (x - \phi)^2]} (x \geq 0, -\infty < \lambda < \infty) \!$
Makeham: $f(x) = (\gamma + \delta\kappa^x)exp(-\gamma x-\frac{\delta (\kappa^x-1)}{log(\kappa)}). x>0 \!$
Hypoexponential: $f(x) = \sum_{i=1}^{n}(1/\alpha_i)exp(-x/\alpha_i)(\prod_{j=1,j\neq i}^{n}\frac{\alpha_i}{\alpha_i-\alpha_j}). x>0 \!$
Doubly Noncentral t: $\!$
Hyperexponential: $f(x) = \sum_{i=1}^{n}\frac{p_i}{\alpha_i}e^{-x/\alpha_i}. x>0 \!$
Muth: $f(x) = (e^{\kappa x}-\kappa)e^{-(1/\kappa)e^{\kappa x}+\kappa x+1/\kappa}. x>0 \!$
Error: $f(x) = \frac{exp[-(|x-a|/b)^{2/c}/2]}{b 2^{c/2+1}\Gamma(1+c/2)}. -\infty < x < \infty \!$
Minimax: $f(x) = \beta\gamma x^{\beta-1}(1-x^\beta)^{\gamma-1}. 0<x<1 \!$
Noncentral F: $f(x) = \sum_{i=0}^{\infty}\frac{\Gamma(\frac{2i+n_1+n_2}{2})(n_1/n_2)^{(2i+n_1)/2}x^{(2i+n_1-2)/2}e^{-\delta/2}(\delta/2)^i}{\Gamma(n_2/2)\Gamma(\frac{2i+n_1}{2})i!(1+\frac{n_1}{n_2}x)^{(2i+n_1+n_2)/2}}. x>0 \!$
IDB: $f(x) = \frac{(1+\kappa x)\delta x+\gamma}{(1+\kappa x)^{\gamma/\kappa+1}}e^{-\delta x^2/2}. x>0 \!$
Standard Power: $f(x) = \beta x^{\beta-1}. 0<x<1 \!$
Rayleigh: $f(x) = \frac{2x}{\alpha}e^{-x^2/\alpha}. x>0 \!$
Standard Triangular: $f(x) = \begin{cases} x+1, -1<x<0 \\ 1 - x, 0 \leq x<1 \end{cases} \!$
Doubly noncentral F: $f(x)= \sum_{j=0}^{\infty}\sum_{k=0}^{\infty}[\frac{e^{-\delta/2}(\frac{1}{2}\delta)^j}{j!}][\frac{e^{-\gamma/2}(\frac{1}{2}\gamma)^k}{k!}]\times n_1^{(n_1/2)+j}n_2^{(n_2/2)+k}x^{(n_1/2)+j-1}\times (n_2+n_1 x)^{-\frac{1}{2}(n_1+n_2)-j-k}\times [B(\frac{1}{2}n_1+j,\frac{1}{2}n_2+k)]^{-1}. x>0 \!$
Power: $f(x)=\frac{\beta x^{\beta-1}}{\alpha^\beta}. 0<x<\alpha \!$
Weibull: $f(x)=(\beta/\alpha)x^{\beta-1}exp[-(1/\alpha)x^\beta]. x>0 \!$
Log-logistic: $f(x)=\frac{\lambda \kappa(\lambda x)^{\kappa-1}}{[1+(\lambda x)^\kappa]^2}. x>0 \!$
TSP: $f(x) = \begin{cases} \frac{n}{b-a}(\frac{x-a}{m-a})^{n-1}, a<x\le m \\ \frac{n}{b-a}(\frac{b-x}{b-m})^{n-1}, m\le x<b \end{cases} \!$
Extreme value: $f(x)=(\beta/\alpha)e^{x\beta-e^{x\beta}/\alpha}. -\infty<x<\infty \!$
Lomax: $f(x)=\frac{\lambda \kappa}{(1+\lambda x)^{\kappa+1}}. x>0 \!$
von Mises: $f(x)=\frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)}. 0<x<2\pi, 0<\mu<2\pi) \!$
Generalized Pareto: $f(x)=(\gamma+\frac{\kappa}{x+\delta})(1+x/\delta)^{-\kappa}e^{-\gamma x}. x>0 \!$
Triangular: $f(x)=\begin{cases} \frac{2(x-a)}{(b-a)(m-a)}, a<x<m \\ \frac{2(b-x)}{(b-a)(b-m)}, m \le x<b \end{cases}. a<m<b \!$
Kolmogorov-Smirnov: $\!$
Exponential Power: $f(x)=(e^{1-e^{\lambda x^\kappa}})e^{\lambda x^\kappa}\lambda \kappa x^{\kappa-1}. x>0 \!$
Lévy distribution: $L_{\alpha ,\gamma } (y)={1\over \pi } \int _{0}^{\infty }e^{-\gamma q^{\alpha } } \cos (qy) dq , y\in {\rm R} , \gamma >0 , 0<\alpha <2$
Modified Power Series distributon: $P(X=x)={a(x)\left\{u(c)\right\}^{x} \over A(c)}$ where $A(c)=\sum _{x}a(x)\left\{u(c)\right\}^{x} ,a(x)\ge 0$
Positive binomial distribution: $P(X=x)=\binom{n}{x}{p^{x} q^{n-x} \over (1-q^{n} )}$ where $x = 1,2,..., n$
Basic Lagrangian distribution of the first kind (BLD1): $P(X=x)={1\over x!} \left[{\partial ^{x-1} \over \partial z^{x-1} } (g(z))^{x} \right]_{z=0}$ where $g (z)$ is pgf , $g (0)$ is not 0
General Basic Lagrangian distribution of the first kind (GLD1): $P(X=0)=f(0) , P(X=x)={1\over x!} \left[{\partial ^{x-1} \over \partial z^{x-1} } \left\{(g(z))^{x} {\partial f(z)\over \partial z} \right\}\right]_{z=0} , x>0$ Where f(z) and g(z) are pgf , $\left[{\partial ^{x-1} \over \partial z^{x-1} } \left\{(g(z))^{x} {\partial f(z)\over \partial z} \right\}\right]_{z=0} >0$ for $x\ge 1$
Binomial-delta distribution: $P(X=x)={n\over x}\binom{{mx}}{x-n}p^{x-n} q^{n+mx-x}$ for $x\ge n$
Binomial-Poisson distribution: $P(X=x)=e^{-M} {(Mq^{m} )^{x} \over x!} {}_{2} F{}_{0} [1-x,-mx;{p\over Mq} ]$ , for $x\ge 0$
Binomial-negative-binomial distribution: $P(X=x)={\Gamma (k+x)\over x!\Gamma (x)} Q^{-k} \left({Pq^{m} \over Q} \right)^{x} {}_{2} F_{1} [1-x,-mx;1-x-k;{-pQ\over qP} ]$ for $x\ge 0$
_Distribution.html Poisson-delta distribution: $P(X=x)={n\over x} {e^{-\theta x} (\theta x)^{x-n} \over (x-n)}$ for $x\ge n$
Poisson-Poisson distribution(also called "Generalized Poisson distribution"): $P (X = x) = M (M + θ x) x - 1 e - (M + θ x) / x!$ for $x\ge 0$
Poisson-binomial distribution: $P(X=x)={(\theta x)^{x-1} \over x!} e^{-\theta x} npq^{n-1} {}_{2} F_{0} [1-x,1-n;{p\over \theta qx} ] , x\ge 1$
Poisson-negative-binomial distribution: $P(X=x)={(\theta x)^{x-1} \over x!} e^{-\theta x} kPQ^{-k-1} {}_{2} F_{0} [1-x,1+k;{-P\over \theta Qx} ] , x\ge 1$
Negative-binomial-delta distribution: $P(X=x)={n\over x} {\Gamma (kx+x-1)\over (x-n)!\Gamma (kx)} \left({P\over Q} \right)^{x-n} Q^{-kx} , x\ge n$
Negative-binomial-Poisson distribution: $P(X=x)={e^{-M} M^{x} \over x!} Q^{-kx} {}_{2} F_{0} [1-x,kx;-;{-P\over MQ} ]$ , for $x\ge 0$
Negative-binomial-binomial distribution: $P (X = 0) = q n$ , $P(X=x)=npq^{n-1} {\Gamma (kx+x-1)\over x!\Gamma (kx)} \left({P\over Q} \right)^{x-1} Q^{-kx} {}_{2} F_{1} [1-x,1-n;2-x-kx;{-pQ\over Pq} ]$ for $x\ge 1$
Negative-binomial-negative-binomial distribution: $P(X=x)=(Q')^{-M} \left({P'\over Q'Q^{k} } \right)^{x} {\Gamma (M+x)\over x!\Gamma (M)} {}_{2} F_{1} [1-x,kx;1-M-x;{PQ'\over P'Q} ]$ for $x\ge 1$
Weight binomial distribution: $P(X=x)=w(x)p_{x} /\sum _{x}^{}w(x)p_{x}$
Positive Poisson distribution (conditional Poisson distribution): $P (X = x) = (e θ - 1) - 1 θ x / x!, x = 1,2,......$
Left-truncated Poisson distribution: $P(X=x)={e^{-\theta } \theta ^{x} \over x!} \left[1-e^{-\theta } \sum _{j=0}^{r_{1} -1}{\theta ^{j} \over j!} \right]^{-1} , x=r_{1} ,r_{1} +1,...$
Right-truncated Poisson distribution: $P(X=x)={\theta ^{x} \over x!} \left[\sum _{j=0}^{r_{2} }{\theta ^{j} \over j!} \right]^{-1} , x=0,1,...,r_{2}$
Doubly-truncated Poisson distribution: $P(X=x)={\theta ^{x} \over x!} \left[\sum _{j=r_{1} }^{r_{2} }{\theta ^{j} \over j!} \right]^{-1} , x=r_{1} ,r_{1} +1,...,r_{2} , 0<r_{1} <r_{2}$
Misrecorded Poisson distribution: $P(X=0)=\omega +(1-\omega )e^{-\theta }, P(X=x)=(1-\omega ){e^{-\theta } \theta ^{x} \over x!} , x\ge 1$

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 $E x p (λ)$ .
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: $\begin{vmatrix}\mu = np \\ n \rightarrow \infty \end{vmatrix}$
Multinomial to Binomial Transformation: $\begin{vmatrix} k=2 \end{vmatrix}$
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 Transformation: $Z=\lim_{\nu\to\infty}T$
Noncentral Student's T to Student's T Transformation: $\mu = 0 \$
Standard Uniform to Pareto Transformation: $\lambda X ^{-1/K} \$
Standard Uniform to Benford Transformation: $10^X \$
Standard Uniform to Exponential Transformation: $n(1-X_{(n)}), n -> \infty$
Standard Uniform to Log Logistic Transformation: $\frac{1}{\lambda}(\frac{1-X}{X})^{1/k}$
Standard Uniform to Standard Triangular Transformation: $X 1 - X 2$
Standard Uniform to Logistic Exponential Transformation: $\frac{log[1+(\frac{X}{1-X})^{1/K}]}{\lambda}$
Standard Uniform to Beta Transformation: If X has a standard uniform distribution, $Y = 1 - X^{1/n} \$ has a beta distribution
Beta to Standard Uniform Transformation: $β = γ = 1$
Continuous Uniform to Standard Uniform Transformation: $a = 0, b = 1 \$
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 \$
Poisson to Normal: $\sigma ^2 = \mu , \mu \to \infty$
Binomial to Poisson: $\mu = np, \mu \to \infty$
Gamma to Inverted Gamma: $\frac{1}{X}$
Fisher-Tippett to Gumbel: $\mu = 0, \beta = 1 \$
Hypergeometric to Binomial: $p = \frac{n_1}{n_3}, n = n_2, n_3, n \to \infty \$
Log-Normal to Normal: $log(X) \$
Normal to Log-Normal: $e^X \$
Log-Normal to Gibrat's: $\mu = 0, x = 1 \$
Cauchy to Standard Cauchy: $\gamma = 1, x_0 = 0 \$
Standard Cauchy to Cauchy: $x_0 + \gamma X \$
Standard Cauchy to Hyperbolic Secant: $\frac{log|x|}{\pi} \$
Beta to Standard Power: $\alpha=\beta, \beta=1 \$
Power series to Pascal: $A(c)=(1-c)^{-x}, c=1-p \$
Gamma Poisson to Pascal: $\alpha=(1-p)/p, \beta=n \$
Poisson to Gamma Poisson: $\mu \sim gamma \$
Discrete uniform to Rectangular: $a=0, b=n\$
beta binomial to rectangular: $a=b=1 \$
beta binomial to negative hypergeometric: $n=n_1, a=n_2, b=n_3 \$
Zipf to Zeta: $n\to\infty\$
Power series to Logarithm: $A(c)=-log(1-c)\$
Power series to Poisson: $A(c)=e^c, \mu=c\$
Pascal to Beta pascal: $p\sim beta\$
pascal to poisson: $\mu=n/p, n\to\infty\$
binomial to beta binomial: $p\sim beta, \mu=np, n\to\infty\$
negative hypergeometric to binomial: $p=n_1/n_3, n_3\to\infty, n_1\to\infty,n_2=n\$
Polya to Binomial: $\beta=0\$
Pascal to geometric: $n=1 \$
geometric to pascal: $\sum{X_i}\$
discrete weibull to geometric: $\beta=1\$
pascal to normal: $\mu=n(1-p), n\to\infty\$
normal to standard normal: $\mu=0, \sigma=1\$
normal to noncentral_chi-square: $\sum{X_i^2/{\sigma}^2}\$
Normal to Chi-square: $(iid) \sum (\frac{x_i-\mu}{\sigma})^2\$
Beta to Normal: $\beta=\gamma \to \infty \$
Normal to Gamma-normal: $\sigma \sim Inverted \ gamma \$
Standard Normal to Standard Cauchy: $\frac{X_1}{X_2} \$
Inverse Gaussian to Standard normal: $\lambda \to \infty \$
Noncentral chi-square to Chi-square: $\delta=0 \$
Gamma to Log gamma: $log X \$
Generalized gamma to Log normal: $\beta \to \infty \$
Generalized gamma to Gamma: $\gamma=1 \$
Inverse Gaussian to Standard Wald: $\mu=1 \$
Inverse Gaussian to Chi-square: $\lambda(X-\mu)^2/(\mu^2 X)\$
Chi-square to Chi: $\sqrt{X}\$
Chi-square to F: $\frac{X_1/n_1}{X_2/n_2}\$
F to Chi-square: $n_1 X, n_2 \to \infty \$
Exponential to Chi-square: $(iid) \frac{2}{\alpha} \sum {X_i}\$
Chi-square to Exponential: $\alpha=2, n=2 \$
Chi-square to Erlang: $n \ even\$
Gamma to Chi-square: $n=2\beta, \alpha=2 \$
Beta to Standard Uniform: $\beta=\gamma=1 \$
Gamma to Erlang: $\beta=n \$
Gamma to Inverted Beta: $X_1/X_2, \alpha=1 \$
Beta to Inverted Beta: $\frac{X}{1-X} \$
Cauchy to Arctangent: $zero \ truncate \$
Hypoexponential to Erlang: $\vec \alpha=\alpha \$
Exponential to Hypoexponential: $\sum X_i\$
Erlang to Exponential: $n=1 \$
Makeham to Gompertz: $\gamma=0 \$
Doubly noncentral t to Noncentral t: $\gamma=0 \$
Exponential to F: $\alpha=1, X_1/X_2\$
Noncentral F to F: $\delta \to 0\$
Exponential to Hyperexponential: $Mixture\$
Hyperexponential to Exponential: $\vec \alpha=\alpha \$
IDB to Exponential: $\delta=\kappa \to 0, \alpha=1/ \gamma \$
Exponential to Rayleigh: $X^2\$
Weibull to Exponential: $\beta=1 \$
Exponential to Weibull: $X^{1/\beta}\$
Muth to Exponential: $\alpha=1, \kappa \to 0 \$
Standard uniform to Gompertz: $\frac{log[1-(log X)(log \kappa)/\delta]}{log \kappa}\$
Standard uniform to Exponential Power: $[log(1-log(1-X))/\gamma]^{1/\kappa}\$
Error to Laplace: $a=0, b=\alpha/2, c=2\$
Laplace to Error: $\alpha_1=\alpha_2 \$
Standard uniform to log logistic: $\frac{1}{\lambda}(\frac{1-X}{X})^{1/\kappa} \$
Standard uniform to Standard triangular: $X_1-X_2 \$
Standard uniform to uniform: $a+(b-a)X \$
Standard uniform to standard power: $X^{1/\beta} \$
Standard power to standard uniform: $\beta=1 \$
Standard uniform to standard power: $X_(n) \$
Minimax to standard power: $\gamma=1 \$

IDB to Rayleigh: $\delta=2/\alpha, \gamma=0 \$
Power to Standard Power: $\alpha=1 \$
Weibull to Rayleigh: $\beta=2 \$
Generalized Pareto to Pareto: $\gamma=0, X+\delta \$
Triangular to standard triangular: $a=-1,b=1,m=0\$
Weibull to Extreme-value: $logX \$
Log logistic to lomax: $\kappa=1 \$
logistic_Distribution.html Lomax to log logistic: $\kappa=1 \$
Log logistic to logistic: $logX \$
TSP to triangular: $n=2 \$
von Mises to Uniform: $\kappa \to 0 \$
Lévy to Cauchy: $\alpha =1 \$
Lévy to Gaussian: $\alpha \to 2$
Modified Power Series to Power series: $u(c)=c \$
BLD1 to Geometric: $g(z)=1-p+pz \$ where $0<p<1 \$
BLD1 to Borel-Tanner: $g(z)=e^{\lambda (z-1)} , 0<\lambda \$
GLD1 to Binomial: $g(z)=1 \$ and $f(z)=(q'+p'z)^{n} \$ where $q'=1-p' \$ , $0<p'<1 \$ , and n is positive integer.
GLD1 to Negative binomial: $g(z)=1 \$ and $f(z)=(q'+p'z)^{n} \$ where $q'=1+P \$ , $0<P \$ , and $n=-k<0 \$
GLD1 to Binomial-delta: $g(z)=(q+pz)^{m} \$ , $f(z)=z^{n} \$ , $mp<1 \$
GLD1 to Binomial-Poisson: $: g(z)=(q+pz)^{m} \$ , $f(z)=e^{M(z-1)} \$ , $mp<1 \$
GLD1 to Binomial-negative-binomial: $g(z)=(q+pz)^{m} \$ , $f(z)=(Q-Pz)^{-k} \$ , $mp<1 \$
GLD1 to Poisson-delta: $g(z)=e^{\theta (z-1)} \$ , $f(z)=z^{n} \$ , $\theta <1 \$
GLD1 to Poisson-Poisson: $g(z)=e^{\theta (z-1)} , f(z)=e^{M(z-1)} , \theta <1 \$
GLD1 to Poisson-binomial: $g(z)=e^{\theta (z-1)} , f(z)=(q+pz)^{n} , \theta <1 \$
GLD1 to Poisson-negative-binomial: $g(z)=e^{\theta (z-1)} , f(z)=(Q-Pz)^{-k} , \theta <1 \$
GLD1 to Negative-binomial-delta: $g(z)=(Q-Pz)^{-k} , f(z)=z^{n} , kP<1 \$
GLD1 to Negative-binomial-Poisson: $g(z)=(Q-Pz)^{-k} , f(z)=e^{M(z-1)} , kP<1 \$
GLD1 to Negative-binomial-binomial: $g(z)=(Q-Pz)^{-k} , f(z)=(q+pz)^{n} , kP<1 \$
GLD1 to Negative-binomial-negative-binomial: $g(z)=(Q-Pz)^{-k} , f(z)=(Q'-P'z)^{-M} , kP<1 \$
Chi-Square to Poisson: $\left(1-F_{\chi _{2(x+1)}^{2} } (2t/\tau )\right)-\left(1-F_{\chi _{2x}^{2} } (2t/\tau )\right) \$ and $\lambda =t/\tau \$
Left-truncated Poisson to Positive Poisson: $r_{1} =1 \$
Doubly-truncated Poisson to Right-truncated Poisson: $r_{1} =0 \$

SOCR Home page: http://www.socr.ucla.edu

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@@ Line 95: / Line 95: @@
 * [http://socr.ucla.edu/htmls/dist/Kolmogorov_Distribution.html Kolmogorov-Smirnov]: <math>  \!</math>
 * [http://socr.ucla.edu/htmls/dist/Exponential-power_Distribution.html Exponential Power]: <math> f(x)=(e^{1-e^{\lambda x^\kappa}})e^{\lambda x^\kappa}\lambda \kappa x^{\kappa-1}. x>0 \!</math>
+* [http://socr.ucla.edu/htmls/dist/Lévy_Distribution.html Lévy distribution]: <math> L_{\alpha ,\gamma } (y)={1\over \pi } \int _{0}^{\infty }e^{-\gamma q^{\alpha } } \cos (qy) dq     ,            y\in {\rm R} , \gamma >0 , 0<\alpha <2  </math>
+* [http://socr.ucla.edu/htmls/dist/Modified-Power-Series_Distribution.html Modified Power Series distributon]: <math> P(X=x)={a(x)\left\{u(c)\right\}^{x} \over A(c)}    </math>  where  <math> A(c)=\sum _{x}a(x)\left\{u(c)\right\}^{x}  ,a(x)\ge 0 </math>
+* [http://socr.ucla.edu/htmls/dist/Positive-binomial_Distribution.html Positive binomial distribution]: <math> P(X=x)=\binom{n}{x}{p^{x} q^{n-x} \over (1-q^{n} )} </math>          where    <math>  x=1,2,...,n </math>
+* [http://socr.ucla.edu/htmls/dist/Basic-Lagrangian-distribution-of-the-first-kind.html Basic Lagrangian distribution of the first kind (BLD1)]: <math> P(X=x)={1\over x!} \left[{\partial ^{x-1} \over \partial z^{x-1} } (g(z))^{x} \right]_{z=0} </math>   where <math>   g(z) </math> is pgf , <math> g(0) </math> is not 0
+* [http://socr.ucla.edu/htmls/dist/General-Basic-Lagrangian-distribution-of-the-first-kind.html General Basic Lagrangian distribution of the first kind (GLD1)]: <math> P(X=0)=f(0) ,
+P(X=x)={1\over x!} \left[{\partial ^{x-1} \over \partial z^{x-1} } \left\{(g(z))^{x} {\partial f(z)\over \partial z} \right\}\right]_{z=0}  ,    x>0</math> Where f(z) and g(z) are pgf  ,  <math>\left[{\partial ^{x-1} \over \partial z^{x-1} } \left\{(g(z))^{x} {\partial f(z)\over \partial z} \right\}\right]_{z=0} >0</math> for <math>x\ge 1</math>
+* [http://socr.ucla.edu/htmls/dist/Binomial-delta_Distribution.html Binomial-delta distribution]: <math> P(X=x)={n\over x}\binom{{mx}}{x-n}p^{x-n} q^{n+mx-x} </math>   for   <math>x\ge n</math>
+* [http://socr.ucla.edu/htmls/dist/Binomial-Poisson_Distribution.html Binomial-Poisson distribution]: <math> P(X=x)=e^{-M} {(Mq^{m} )^{x} \over x!} {}_{2} F{}_{0} [1-x,-mx;{p\over Mq} ] </math>   ,    for   <math>x\ge 0 </math>
+* [http://socr.ucla.edu/htmls/dist/Binomial-negative-binomial_Distribution.html Binomial-negative-binomial distribution]: <math> P(X=x)={\Gamma (k+x)\over x!\Gamma (x)} Q^{-k} \left({Pq^{m} \over Q} \right)^{x} {}_{2} F_{1} [1-x,-mx;1-x-k;{-pQ\over qP} ] </math>  for   <math>x\ge 0</math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-delta _Distribution.html Poisson-delta distribution]: <math> P(X=x)={n\over x} {e^{-\theta x} (\theta x)^{x-n} \over (x-n)} </math>    for  <math>x\ge n </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-Poisson_Distribution.html Poisson-Poisson distribution(also called "Generalized Poisson distribution")]: <math> P(X=x)=M(M+\theta x)^{x-1} e^{-(M+\theta x)} /x! </math>   for  <math>x\ge 0 </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-binomial_Distribution.html Poisson-binomial distribution]: <math> P(X=x)={(\theta x)^{x-1} \over x!} e^{-\theta x} npq^{n-1} {}_{2} F_{0} [1-x,1-n;{p\over \theta qx} ]    ,    x\ge 1</math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-negative-binomial_Distribution.html Poisson-negative-binomial distribution]: <math> P(X=x)={(\theta x)^{x-1} \over x!} e^{-\theta x} kPQ^{-k-1} {}_{2} F_{0} [1-x,1+k;{-P\over \theta Qx} ]    ,   x\ge 1</math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-delta_Distribution.html Negative-binomial-delta distribution]: <math> P(X=x)={n\over x} {\Gamma (kx+x-1)\over (x-n)!\Gamma (kx)} \left({P\over Q} \right)^{x-n} Q^{-kx}     ,   x\ge n </math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-Poisson_Distribution.html Negative-binomial-Poisson distribution]: <math> P(X=x)={e^{-M} M^{x} \over x!} Q^{-kx} {}_{2} F_{0} [1-x,kx;-;{-P\over MQ} ] </math> ,  for  <math>x\ge 0</math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-binomial_Distribution.html Negative-binomial-binomial distribution]: <math> P(X=0)=q^{n} </math> , <math>P(X=x)=npq^{n-1} {\Gamma (kx+x-1)\over x!\Gamma (kx)} \left({P\over Q} \right)^{x-1} Q^{-kx} {}_{2} F_{1} [1-x,1-n;2-x-kx;{-pQ\over Pq} ] </math>  for <math>x\ge 1</math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-negative-binomial_Distribution.html Negative-binomial-negative-binomial distribution]: <math> P(X=x)=(Q')^{-M} \left({P'\over Q'Q^{k} } \right)^{x} {\Gamma (M+x)\over x!\Gamma (M)} {}_{2} F_{1} [1-x,kx;1-M-x;{PQ'\over P'Q} ] </math> for <math>x\ge 1</math>
+* [http://socr.ucla.edu/htmls/dist/Weight-binomial_Distribution.html Weight binomial distribution]: <math> P(X=x)=w(x)p_{x} /\sum _{x}^{}w(x)p_{x}</math>
+* [http://socr.ucla.edu/htmls/dist/Positive-Poisson_Distribution.html Positive Poisson distribution (conditional Poisson distribution)]: <math> P(X=x)=(e^{\theta } -1)^{-1} \theta ^{x} /x! , x=1,2,......</math>
+* [http://socr.ucla.edu/htmls/dist/Left-truncated-Poisson_Distribution.html Left-truncated Poisson distribution]: <math> P(X=x)={e^{-\theta } \theta ^{x} \over x!} \left[1-e^{-\theta } \sum _{j=0}^{r_{1} -1}{\theta ^{j} \over j!}  \right]^{-1} , x=r_{1} ,r_{1} +1,...</math>
+* [http://socr.ucla.edu/htmls/dist/Right-truncated-Poisson_Distribution.html Right-truncated Poisson distribution]: <math> P(X=x)={\theta ^{x} \over x!} \left[\sum _{j=0}^{r_{2} }{\theta ^{j} \over j!}  \right]^{-1} , x=0,1,...,r_{2}</math>
+* [http://socr.ucla.edu/htmls/dist/Doubly-truncated-Poisson_Distribution.html Doubly-truncated Poisson distribution]: <math> P(X=x)={\theta ^{x} \over x!} \left[\sum _{j=r_{1} }^{r_{2} }{\theta ^{j} \over j!}  \right]^{-1} , x=r_{1} ,r_{1} +1,...,r_{2} , 0<r_{1} <r_{2}</math>
+* [http://socr.ucla.edu/htmls/dist/Misrecorded-Poisson_Distribution.html Misrecorded Poisson distribution]: <math> P(X=0)=\omega +(1-\omega )e^{-\theta }, P(X=x)=(1-\omega ){e^{-\theta } \theta ^{x} \over x!} , x\ge 1</math>
 ==Transformations==
@@ Line 148: / Line 171: @@
 * [http://socr.ucla.edu/htmls/dist/HyperbolicSecant_Distribution.html Standard Cauchy to Hyperbolic Secant]: <math> \frac{log|x|}{\pi} \ </math>
 * [http://socr.ucla.edu/htmls/dist/Standard_Power.html Beta to Standard Power]: <math> \alpha=\beta, \beta=1 \ </math>
-* [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Normal to Chi-square]: <math> (iid) \sum (\frac{x_i-\mu}{\sigma})^2\ </math>
 * [http://en.wikipedia.org/wiki/Pascal Power series to Pascal]: <math> A(c)=(1-c)^{-x}, c=1-p \ </math>
-* [http://socr.ucla.edu/htmls/dist/Normal_Distribution.html Beta to Normal]: <math> \beta=\gamma \to \infty \ </math>
-* [http://socr.ucla.edu/htmls/dist/Normal_Distribution.html Normal to Gamma-normal]: <math> \sigma \sim Inverted gamma \ </math>
-* [http://socr.ucla.edu/htmls/dist/Cauchy_Distribution.html Standard Normal to Standard Cauchy]: <math> \frac{X_1}{X_2} \ </math>
-* [http://socr.ucla.edu/htmls/dist/Normal_Distribution.html Inverse Gaussian to Standard normal]: <math> \lambda \to \infty \ </math>
 * [http://en.wikipedia.org/wiki/Pacal Gamma Poisson to Pascal]: <math> \alpha=(1-p)/p, \beta=n \ </math>
 * [http://en.wikipedia.org/wiki/Gamma_Poisson Poisson to Gamma Poisson]: <math> \mu \sim gamma \ </math>
@@ Line 169: / Line 187: @@
 * [http://en.wikipedia.org/wiki/Geometric Pascal to geometric]: <math> n=1 \ </math>
 * [http://en.wikipedia.org/wiki/Pascal geometric to pascal]: <math> \sum{X_i}\ </math>
-* [http://en.wikipedia.org/wiki/Geometric discrete weibull to geometric]: <math> \ </math>
+* [http://en.wikipedia.org/wiki/Geometric discrete weibull to geometric]: <math> \beta=1\ </math>
-* [http://en.wikipedia.org/wiki/Normal pascal to normal]: <math> \ </math>
+* [http://en.wikipedia.org/wiki/Normal pascal to normal]: <math> \mu=n(1-p), n\to\infty\ </math>
-* [http://en.wikipedia.org/wiki/standard_normal normal to standard normal]: <math> \ </math>
+* [http://en.wikipedia.org/wiki/standard_normal normal to standard normal]: <math> \mu=0, \sigma=1\ </math>
-* [http://en.wikipedia.org/wiki/Noncentral_chi-square normal to noncentral_chi-square]: <math> \ </math>
+* [http://en.wikipedia.org/wiki/Noncentral_chi-square normal to noncentral_chi-square]: <math> \sum{X_i^2/{\sigma}^2}\ </math>
+* [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Normal to Chi-square]: <math> (iid) \sum (\frac{x_i-\mu}{\sigma})^2\ </math>
+* [http://socr.ucla.edu/htmls/dist/Normal_Distribution.html Beta to Normal]: <math> \beta=\gamma \to \infty \ </math>
+* [http://socr.ucla.edu/htmls/dist/Normal_Distribution.html Normal to Gamma-normal]: <math> \sigma \sim Inverted \ gamma \ </math>
+* [http://socr.ucla.edu/htmls/dist/Cauchy_Distribution.html Standard Normal to Standard Cauchy]: <math> \frac{X_1}{X_2} \ </math>
+* [http://socr.ucla.edu/htmls/dist/Normal_Distribution.html Inverse Gaussian to Standard normal]: <math> \lambda \to \infty \ </math>
 * [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Noncentral chi-square to Chi-square]: <math> \delta=0 \ </math>
 * [http://socr.ucla.edu/htmls/dist/Gamma_Distribution.html Gamma to Log gamma]:<math> log X \ </math>
@@ Line 180: / Line 203: @@
 * [http://socr.ucla.edu/htmls/dist/InverseGaussian_Distribution.html Inverse Gaussian to Standard Wald]:<math> \mu=1 \ </math>
 * [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Inverse Gaussian to Chi-square]:<math> \lambda(X-\mu)^2/(\mu^2 X)\ </math>
+* [http://socr.ucla.edu/htmls/dist/Chi_Distribution.html Chi-square to Chi]:<math> \sqrt{X}\ </math>
+* [http://socr.ucla.edu/htmls/dist/Fisher_Distribution.html Chi-square to F]:<math> \frac{X_1/n_1}{X_2/n_2}\ </math>
+* [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html F to Chi-square]:<math> n_1 X, n_2 \to \infty \ </math>
+* [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Exponential to Chi-square]:<math> (iid) \frac{2}{\alpha} \sum {X_i}\ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Chi-square to Exponential]:<math> \alpha=2, n=2 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Erlang_Distribution.html Chi-square to Erlang]:<math> n \ even\ </math>
+* [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Gamma to Chi-square]:<math> n=2\beta, \alpha=2 \ </math>
+* [http://socr.ucla.edu/htmls/dist/ChiSquare_Distribution.html Beta to Standard Uniform]:<math> \beta=\gamma=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Erlang_Distribution.html Gamma to Erlang]:<math> \beta=n \ </math>
+* [http://socr.ucla.edu/htmls/dist/Gamma_Distribution.html Gamma to Inverted Beta]:<math> X_1/X_2, \alpha=1  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Beta_Distribution.html Beta to Inverted Beta]:<math> \frac{X}{1-X} \ </math>
+* [http://socr.ucla.edu/htmls/dist/Cauchy_Distribution.html Cauchy to Arctangent]:<math> zero \ truncate \ </math>
+* [http://socr.ucla.edu/htmls/dist/Erlang_Distribution.html Hypoexponential to Erlang]:<math> \vec \alpha=\alpha \ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Exponential to Hypoexponential]:<math> \sum X_i\ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Erlang to Exponential]:<math> n=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Gompertz_Distribution.html Makeham to Gompertz]:<math> \gamma=0 \ </math>
+* [http://socr.ucla.edu/htmls/dist/StudentT_Distribution.html Doubly noncentral t to Noncentral t]:<math> \gamma=0 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Fisher_Distribution.html Exponential to F]:<math> \alpha=1, X_1/X_2\ </math>
+* [http://socr.ucla.edu/htmls/dist/Fisher_Distribution.html Noncentral F to F]:<math> \delta \to 0\ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Exponential to Hyperexponential]:<math> Mixture\ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Hyperexponential to Exponential]:<math> \vec \alpha=\alpha \ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html IDB to Exponential]:<math>\delta=\kappa \to 0, \alpha=1/ \gamma \ </math>
+* [http://socr.ucla.edu/htmls/dist/Rayleigh_Distribution.html Exponential to Rayleigh]:<math> X^2\ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Weibull to Exponential]:<math> \beta=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Weibull_Distribution.html Exponential to Weibull]:<math> X^{1/\beta}\ </math>
+* [http://socr.ucla.edu/htmls/dist/Exponential_Distribution.html Muth to Exponential]:<math> \alpha=1, \kappa \to 0 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Gompertz_Distribution.html Standard uniform to Gompertz]:<math> \frac{log[1-(log X)(log \kappa)/\delta]}{log \kappa}\ </math>
+* [http://socr.ucla.edu/htmls/dist/ContinuousUniform_Distribution.html Standard uniform to Exponential Power]:<math> [log(1-log(1-X))/\gamma]^{1/\kappa}\ </math>
+* [http://socr.ucla.edu/htmls/dist/Laplace_Distribution.html Error to Laplace]:<math> a=0, b=\alpha/2, c=2\ </math>
+* [http://socr.ucla.edu/htmls/dist/Error_Distribution.html Laplace to Error]:<math> \alpha_1=\alpha_2 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Log-logistic_Distribution.html Standard uniform to log logistic]:<math> \frac{1}{\lambda}(\frac{1-X}{X})^{1/\kappa} \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-triangular_Distribution.html Standard uniform to Standard triangular]:<math> X_1-X_2 \ </math>
+* [http://socr.ucla.edu/htmls/dist/uniform_Distribution.html Standard uniform to uniform]:<math> a+(b-a)X \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-Power_Distribution.html Standard uniform to standard power]:<math> X^{1/\beta} \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-uniform_Distribution.html Standard power to standard uniform]:<math> \beta=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-power_Distribution.html Standard uniform to standard power]:<math> X_(n) \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-power_Distribution.html Minimax to standard power]:<math> \gamma=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Rayleigh_Distribution.html IDB to Rayleigh]:<math> \delta=2/\alpha, \gamma=0 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-Power_Distribution.html Power to Standard Power]:<math> \alpha=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Rayleigh_Distribution.html Weibull to Rayleigh]:<math> \beta=2 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Pareto_Distribution.html Generalized Pareto to Pareto]:<math> \gamma=0, X+\delta \ </math>
+* [http://socr.ucla.edu/htmls/dist/Standard-triangular_Distribution.html Triangular to standard triangular]:<math> a=-1,b=1,m=0\ </math>
+* [http://socr.ucla.edu/htmls/dist/Extreme-value_Distribution.html Weibull to Extreme-value]:<math> logX \ </math>
+* [http://socr.ucla.edu/htmls/dist/Lomax_Distribution.html Log logistic to lomax]:<math> \kappa=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Log logistic_Distribution.html Lomax to log logistic]:<math> \kappa=1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Logistic_Distribution.html Log logistic to logistic]:<math> logX \ </math>
+* [http://socr.ucla.edu/htmls/dist/Triangular_Distribution.html TSP to triangular]:<math> n=2 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Uniform_Distribution.html von Mises to Uniform]:<math> \kappa \to 0 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Cauchy_Distribution.html Lévy to Cauchy]:<math> \alpha =1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Gaussian_Distribution.html Lévy to Gaussian]:<math> \alpha \to 2</math>
+* [http://socr.ucla.edu/htmls/dist/Power-series_Distribution.html Modified Power Series to Power series]:<math> u(c)=c  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Geometric_Distribution.html BLD1 to Geometric]:<math> g(z)=1-p+pz \ </math> where<math>0<p<1  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Borel-Tanner_Distribution.html BLD1 to Borel-Tanner]:<math> g(z)=e^{\lambda (z-1)}  , 0<\lambda  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Binomial_Distribution.html GLD1 to Binomial]:<math> g(z)=1 \ </math>  and   <math>f(z)=(q'+p'z)^{n}  \ </math>   where  <math>q'=1-p' \ </math>  ,  <math>0<p'<1 \ </math>, and n is positive integer.
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial_Distribution.html GLD1 to Negative binomial]:<math> g(z)=1 \ </math>      and   <math>      f(z)=(q'+p'z)^{n} \ </math>        where  <math>      q'=1+P \ </math>  , <math>      0<P \ </math> , and <math>  n=-k<0 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Binomial-delta_Distribution.html GLD1 to Binomial-delta]: <math> g(z)=(q+pz)^{m}  \ </math> , <math>      f(z)=z^{n}  \ </math> , <math> mp<1  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Binomial-Poisson_Distribution.html GLD1 to Binomial-Poisson]:<math> : g(z)=(q+pz)^{m}  \ </math> , <math> f(z)=e^{M(z-1)} \ </math> , <math> mp<1  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Binomial-negative-binomial_Distribution.html GLD1 to Binomial-negative-binomial]:<math> g(z)=(q+pz)^{m} \  </math>  ,  <math> f(z)=(Q-Pz)^{-k} \  </math>  ,  <math> mp<1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-delta_Distribution.html GLD1 to Poisson-delta]: <math> g(z)=e^{\theta (z-1)} \ </math>,   <math> f(z)=z^{n}  \ </math>,  <math> \theta <1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-Poisson_Distribution.html GLD1 to Poisson-Poisson]: <math> g(z)=e^{\theta (z-1)}   ,  f(z)=e^{M(z-1)}   ,  \theta <1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-binomial_Distribution.html GLD1 to Poisson-binomial]: <math> g(z)=e^{\theta (z-1)}   ,  f(z)=(q+pz)^{n} , \theta <1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson-negative-binomial_Distribution.html GLD1 to Poisson-negative-binomial]: <math> g(z)=e^{\theta (z-1)}   ,  f(z)=(Q-Pz)^{-k} , \theta <1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-delta_Distribution.html GLD1 to Negative-binomial-delta]: <math> g(z)=(Q-Pz)^{-k}   ,  f(z)=z^{n}   ,   kP<1  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-Poisson_Distribution.html GLD1 to Negative-binomial-Poisson]: <math> g(z)=(Q-Pz)^{-k}    ,  f(z)=e^{M(z-1)}   , kP<1  \ </math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-binomial_Distribution.html GLD1 to Negative-binomial-binomial]: <math> g(z)=(Q-Pz)^{-k}   ,  f(z)=(q+pz)^{n}   , kP<1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Negative-binomial-negative-binomial_Distribution.html GLD1 to Negative-binomial-negative-binomial]: <math> g(z)=(Q-Pz)^{-k}   ,  f(z)=(Q'-P'z)^{-M}  ,  kP<1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Poisson_Distribution.html Chi-Square to Poisson]: <math> \left(1-F_{\chi _{2(x+1)}^{2} } (2t/\tau )\right)-\left(1-F_{\chi _{2x}^{2} } (2t/\tau )\right) \ </math>       and     <math> \lambda =t/\tau \ </math>
+* [http://socr.ucla.edu/htmls/dist/Positive-Poisson_Distribution.html Left-truncated Poisson to Positive Poisson]: <math> r_{1} =1 \ </math>
+* [http://socr.ucla.edu/htmls/dist/Right-truncated-Poisson_Distribution.html Doubly-truncated Poisson to Right-truncated Poisson]: <math> r_{1} =0 \ </math>
 <hr>
 * SOCR Home page: http://www.socr.ucla.edu
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Formulas

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Current revision as of 12:57, 18 January 2011

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