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Daphne Koller Bayesian Networks Semantics & Factorization Probabilistic Graphical Models Representation.

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Presentation on theme: "Daphne Koller Bayesian Networks Semantics & Factorization Probabilistic Graphical Models Representation."— Presentation transcript:

1 Daphne Koller Bayesian Networks Semantics & Factorization Probabilistic Graphical Models Representation

2 Daphne Koller Grade Course Difficulty Student Intelligence Student SAT Reference Letter

3 Daphne Koller The Student Network IntelligenceDifficulty Grade Letter SAT 0.3 0.08 0.25 0.4 g2g2 0.020.9i 1,d 0 0.70.05i 0,d 1 0.5 0.3 g1g1 g3g3 0.2i 1,d 1 0.3i 0,d 0 l1l1 l0l0 0.99 0.4 0.10.9g1g1 0.01g3g3 0.6g2g2 0.2 0.95 s0s0 s1s1 0.8i1i1 0.05i0i0 0.40.6 d1d1 d0d0 0.30.7 i1i1 i0i0

4 Template vertLeftWhite2 Defining a joint distribution What is the joint distribution P(D,I,G,S,L)? P(D) P(I) P(G) P(S) P(L) P(D) P(I) P(G|I,D) P(S|I) P(L|G) P(D) P(I) P(G|I) P(G|D) P(S|I) P(L|G) IntelligenceDifficulty Grade Letter SAT P(D|G) P(I|D) P(S|I) P(G|L,I,D) P(L|G)

5 Daphne Koller The Chain Rule for Bayesian Nets IntelligenceDifficulty Grade Letter SAT

6 Daphne Koller Bayesian Network A Bayesian network is:

7 Daphne Koller BN Is a Legal Distribution

8 Template vertLeft1 What is the value of 1 P(L) P(G) None of the above

9 Daphne Koller P Factorizes over G Let G be a graph over X 1,…,X n. P factorizes over G if P(X 1,…,X n ) =

10 Daphne Koller Genetic Inheritance Homer Bart Marge LisaMaggie Clancy Jackie Selma

11 Daphne Koller BNs for Genetic Inheritance G Homer G Bart G Marge G Lisa G Maggie G Clancy G Jackie G Selma B Clancy B Jackie B Selma B Homer B Marge B Bart B Lisa B Maggie

12 Daphne Koller END END END

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18 The Chain Rule for Bayesian Nets IntelligenceDifficulty Grade Letter SAT 0.3 0.08 0.25 0.4 g2g2 0.020.9i 1,d 0 0.70.05i 0,d 1 0.5 0.3 g1g1 g3g3 0.2i 1,d 1 0.3i 0,d 0 l1l1 l0l0 0.99 0.4 0.10.9g1g1 0.01g3g3 0.6g2g2 0.2 0.95 s0s0 s1s1 0.8i1i1 0.05 i0i0 0.40.6 d1d1 d0d0 0.30.7 i1i1 i0i0 P(D,I,G,S,L) = P(D) P(I) P(G | I,D) P(L | G) P(S | I)

19 Template vertLeftWhite1 Suppose  is at a local minimum of a function. What will one iteration of gradient descent do? Leave  unchanged. Change  in a random direction. Move  towards the global minimum of J(  ). Decrease .

20 Template vertLeftWhite1 Consider the weight update: Which of these is a correct vectorized implementation?

21 Template vertLeftWhite2 Fig. A corresponds to  =0.01, Fig. B to  =0.1, Fig. C to  =1. Fig. A corresponds to  =0.1, Fig. B to  =0.01, Fig. C to  =1. Fig. A corresponds to  =1, Fig. B to  =0.01, Fig. C to  =0.1. Fig. A corresponds to  =1, Fig. B to  =0.1, Fig. C to  =0.01.

22 Template vertLeftWhite2

23 Template block2x2White1 Ordering of buttons is: 13 24


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