You could try constructing one yourself!
Try working backwards. Let's do the discrete case for simplicity.
Write down a sum you know doesn't converge, and assume that what you wrote is the sum that calculates expected value. Once you do that, it's easy to compute the weight of each individual event.
Of course, to be a probability distribution, the sum of the weights has to be 1. Try computing it. You probably won't get 1... but if you get a finite value, do you see an easy way to modify your distribution so that it's a probability distribution?
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Well, let's try an example. I know that the sum:
0 + 1 + 4 + 9 + 16 + ... = sum_{i = 0 .. infinity} i^2
doesn't converge. Well, if this sum is computing the expected value, then
sum_{i = 0 .. infinity} i^2 = sum_{i = 0 .. infinity} i * P(i)
so, P(i) = i for all events i. Now, to see what the total weight is:
sum_{i = 0 .. infinity} P(i) = +infinity
Oh well, this one doesn't work. We have to try something else.
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