Hi Austin,
You can do this as well, basically by defining the joint distribution P(X,Y) as P(X|Y) * P(Y) (or P(Y|X) * P(X)), i.e. one variable is explicitly defined using a set of conditional distributions. Something like this should work (for discrete valued distributions):
Code:category c = (2, {{0.7,0.3}}, MEAN, , {{1,2}}, 1,2);
category d = (2, {{0.5,0.5}{0.2,0.8}}, MEAN, c , {{3,4}}, 3,4);
The joint probability of P(c,d) is
P(1,3) = 0.7*0.5 = 0.35;
P(1,4) = 0.7*0.5 = 0.35;
P(2,3) = 0.3*0.2 = 0.06;
P(2,4) = 0.3*0.8 = 0.24;
When you define d in the example above, the 2nd argument defines the matrix of conditional probabilities (1st row for the 1st value of c, 2nd row for the 2nd value of c ...) and using 'c' in place of the standard density argument tells HyPhy that d depends on c.
Sergei
HTH,
Sergei