Inverse Gaussian distribution
= Inverse Gaussian distribution
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For an <expected value> parameter $\mu>0$ and a <shape parameter> $\lambda>0$, the inverse Gaussian distribution has <probability density function>
$$
f(x;\mu,\lambda)
=\left(\frac{\lambda}{2\pi x^3}\right)^{1/2}
\exp\left(-\frac{\lambda(x-\mu)^2}{2\mu^2x}\right),
\qquad x>0.
$$
Its mean is $\mu$ and its variance is $\mu^3/\lambda$.