We have recently seen how we can define dependencies between random variables with Archimedean copulas which calculate the probability that they each fall below given values by applying a generator function

Like all copulas they are effectively the CDFs of vector valued random variables whose elements are uniformly distributed when considered independently. Whilst those Archimedean CDFs were relatively trivial to implement, we found that their probability density functions, or PDFs, were somewhat more difficult and that the random variables themselves required some not at all obvious mathematical manipulation to get right.

Having done all the hard work implementing the

*φ*to the results of their cumulative distribution functions, or CDFs, for those values, and applying its inverse to their sum.Like all copulas they are effectively the CDFs of vector valued random variables whose elements are uniformly distributed when considered independently. Whilst those Archimedean CDFs were relatively trivial to implement, we found that their probability density functions, or PDFs, were somewhat more difficult and that the random variables themselves required some not at all obvious mathematical manipulation to get right.

Having done all the hard work implementing the

`ak.archimedeanCopula`

, `ak.archimedeanCopulaDensity`

and `ak.archimedeanCopulaRnd`

functions we shall now use them to implement some specific families of Archimedean copulas.
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