For each element of p, compute the quantile (the inverse of the CDF) of the exponential distribution with mean mu. The size of x is the common size of p and mu. A scalar input functions as a constant matrix of the same size as the other inputs.

Default value is mu = 1.

A common alternative parameterization of the exponential distribution is to use the parameter $\lambda$ defined as the mean number of events in an interval as opposed to the parameter $\mu$, which is the mean wait time for an event to occur. $\lambda$ and $\mu$ are reciprocals, i.e. $\mu \; =\; 1\; /\; \lambda$.

When called with three output arguments, i.e. [x, xlo, xup], expinv computes the confidence bounds for x when the input parameter mu is an estimate. In such case, pcov, a scalar value with the variance of the estimated parameter mu, is necessary. Optionally, alpha, which has a default value of 0.05, specifies the 100 * (1 - alpha) percent confidence bounds. xlo and xup are arrays of the same size as x containing the lower and upper confidence bounds.

Further information about the exponential distribution can be found at https://en.wikipedia.org/wiki/Exponential_distribution

See also: expcdf, exppdf, exprnd, expfit, explike, expstat

Source Code: expinv