DataA: An example for you to test your learning algorithms on (from
Nils Nilsson: Artificial Intelligence: A New Synthesis)


For parts 1 & 2, please use this structure:
G [Parent: B]
B [Parent: None]
M [Parent: B, L]
L [Parent: None]

b -> G
 \
  \
   V
L -> M

Your solutions for part 1 & 2 should be similar to the book's:
The score for this network should be: -216.14.

The MLE CPT's look like this:
G [Parents: B]
1.0    0.0         when B is -
0.0532 0.9468      when B is +

M [Parents: B L]
1.0    0.0         when B is -, L is -
0.9667 0.03333     when B is +, L is -
1.0    0.0         when B is -, L is + 
0.1093 0.89063     when B is +, L is +

B [Parents:]
0.0600 0.94

L [Parents:]
0.32   0.68


G	M	B	L
100 samples
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	1
1	1	1	0
1	0	1	1
1	0	1	1
1	0	1	1
1	0	1	1
1	0	1	1
1	0	1	1
1	0	1	1
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
1	0	1	0
0	1	1	1
0	1	1	1
0	1	1	1
0	0	1	0
0	0	1	0
0	0	0	1
0	0	0	1
0	0	0	1
0	0	0	1
0	0	0	0
0	0	0	0