Need help completing a function that is supposed to represent a belief network. Here is the code that needs to be filled in:

def example_bn():

"""

Returns a collection of `Factor` objects representing the belief network

in the assignment.

"""

return [

# Pr(A)

Factor({'A': (0,1)},

((0, 0.25),

(1, 0.75))),

# TODO

]

Replace # TODO with the rest of the belief network.

Here is a description of the belief network:

So in essence, write down the conditional probability distribution of the belief network, because each tuple represents a row in that table.

Pr(B | G = 0, E = 1) using variable order G, E, A, B, C, D, F. We'll need to find the probability Pr(B = 1 | G = 0, E = 1), so take that into account when making the table if relevant.

(Variable Elimination; 75 points) Consider the belief network below. For every variable V : - dom(V)={0,1} - Pr(A=1)=0.75 - For V=A, the probability that V=1 is 12q, where q=Wparents(V)W. (In other words, the more of V 's parents have value 1 , the more likely V=1 )