Probabilistic Graphical Models 10-708, Spring 2017
https://www.cs.cmu.edu/~epxing/Class/10708-17/slides/lecture1-Introduction.pdf
Computational and CS orientated => DK and NF's book
Statistical and easier one => Jordan's book
MLAPP => also a good book
HWs => Theory, algorithm design and implementation. Very heavy.
N copies of data.
subscript means the dims of features.
bottom right figures
a given presentation + inference => enough for some tasks
learn a representation => a more adv. task
M* = argmax (m \in M) F(D;m)
M*: best representation
m: one representation
F: score function
D: data
one simple case: every random variable X_n is binary: X_n \in {0,1}
O(exp(n)) => bad algorithm
↓↓↓↓↓↓↓↓↓↓(invite a biologist)↓↓↓↓↓↓↓↓↓↓↓
categorize
add pathways
18 vs 2^8
A factorization rule. two resources of variables.
PGM => conditional distribution
GM => pm.Deterministic
If I have P(A,B), how to proof A is independent of B?
Method 1: defactorize P(A,B) = P(A)*P(B)
Method 2: build a graph like the one above, and A and B are automatically independent
Yellow ⊥ Orange | Graph
the yellow node is only linked to its parents, children, and children's coparents (greeen nodes)
⊥: indenpendency
DARPA grand challenge
NLP
biostats
