Inverse of a cdf?

If your input is specified in the form of a non-normalized histogram, then simply using the built-in quantile() function automatically computes the data point for a specified quantile, which is what the inverse-CDF does. If the histogram is normalized by the number of data points (making it a probability vector), then just multiply it by the number of data points first. See here for the quantile() details.

Basically, you'll assume that given your histogram/data, the first parameter is fixed, which turns quantiles() into a function only of the specified probability values p . You could easily write a wrapper function to make it more convenient if necessary. This removes the need to explicitly compute the CDF with cumsum() .

Ok, I think I found a much shorter version, which works at least as fast and as accurately.

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