Last year we took a look at multivariate uniformly distributed random variables, which generalise uniform random variables to multiple dimensions with random vectors whose elements are independently uniformly distributed. We have now seen how we can similarly generalise normally distributed random variables with the added property that the normally distributed elements of their vectors may be dependent upon each other; specifically that they may be correlated.

As it turns out, we can generalise this dependence to arbitrary sets of random variables with a fairly simple observation.

As it turns out, we can generalise this dependence to arbitrary sets of random variables with a fairly simple observation.

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