Some common PDFs:
Normal: $f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}\exp\left[-\frac{(x-\mu)^2}{2\sigma^2}\right]$
Binomial: $Pr(X = x) = \frac{n!}{x!(n-x)!}p^x(1-p)^{n-x}$
Gamma: $f(x) = \frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}\exp(-\beta x)$
PDF stands for Probability Density Function (as compared to CDF for Cumulative Distribution Function). The PDF of a variable gives the likelihood for each value of a continuous variable. Use this tag also for PMF (Probability Mass Function) the analog for discrete variables.
Some common PDFs:
Normal: $f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}\exp\left[-\frac{(x-\mu)^2}{2\sigma^2}\right]$
Binomial: $Pr(X = x) = \frac{n!}{x!(n-x)!}p^x(1-p)^{n-x}$
Gamma: $f(x) = \frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}\exp(-\beta x)$
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