How to use pymc to parameterize a probabilistic graphical model?

问题

How can one use pymc to parameterize a probabilistic graphical model?

Suppose I have a PGM with two nodes X and Y. Lets say X->Y is the graph.

And X takes two values {0,1}, and Y also takes two values {0,1}.

I want to use pymc to learn the parameters of the distribution and populate the graphical model with it for running inferences.

The way I could think of is as follows:

X_p = pm.Uniform("X_p", 0, 1)
X = pm.Bernoulli("X", X_p, values=X_Vals, observed=True)
Y0_p = pm.Uniform("Y0_p", 0, 1)
Y0 = pm.Bernoulli("Y0", Y0_p, values=Y0Vals, observed=True)
Y1_p = pm.Uniform("Y1_p", 0, 1)
Y1 = pm.Bernoulli("Y1", Y1_p, values=Y1Vals, observed=True)

Here Y0Vals are values of Y corresponding to X values = 0 And Y1Vals are values of Y corresponding to X values = 1.

The plan is to draw MCMC samples from these and use the means of Y0_p and Y1_p to populate the discrete bayesian network's probability... So the probability table for P(X) = (X_p,1-X_p) while that of P(Y/X):

  Y  0       1
X 
0   Y0_p   1-Y0_p
1   Y1_p   1-Y1_p

Questions:

1. Is this the correct way of doing this?
2. Does not this get clumsy, especially if I have X having 100s of discrete values? or if a variable has two parents X and Y with 10 discrete values each?
3. Is there something better I can do?
4. Are there any good books that detail how we can do this kind of interconnection.

来源：https://stackoverflow.com/questions/37658557/how-to-use-pymc-to-parameterize-a-probabilistic-graphical-model