<transcript><audio_25>X as being the number on a dice and Y is equal to 6 minus that number on that - you'll see they the two will be very almost in contrast as x increases Y is decreasing so this is our maximum copula</transcript><transcript><audio_26>and it forms an upper bound it only exists in the bivariate case because as soon as you have more than two probabilities then things start getting a little bit awkward with something being fully negative dependency can actually be if this there's 3 things you</transcript><transcript><audio_27>know if one is the exact opposite of the other then what is the third thing so it only exists in the bivariate case so we can kind of see you know if f of X is equal to 4 then F of Y has to be equal</transcript> X as being the number on a dice and Y is equal to 6 minus that number on that - you'll see they the two will be very almost in contrast as x increases Y is decreasing so this is our maximum copula and it forms an upper bound it only exists in the bivariate case because as soon as you have more than two probabilities then things start getting a little bit awkward with something being fully negative dependency can actually be if this there's 3 things you know if one is the exact opposite of the other then what is the third thing so it only exists in the bivariate case so we can kind of see you know if f of X is equal to 4 then F of Y has to be equal
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