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Grids A1,1 A1,2 A1,3 A1,4 A2,1 A2,2 A2,3 A2,4 A3,1 A3,2 A3,3 A3,4 A4,1 A4,2 A4,3 A4,4
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1: A,B 4: A,D A B D 2: B,C 3: C,D C 1,4 = 1 4,1 = 1 1,2 = 1 1
3,4 = B D 2,1 = 1 4,3 = 1 2: B,C 3: C,D C 2,3 = 1 3,2 = 1
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1: A,B 4: A,D A B D 2: B,C 3: C,D C 1,4 = B 2,1(B) y1(A,B) 4,1 =
D 3,4(D) y4(A,D) 1: A,B 4: A,D A 1,2 = A 4,1(A) y1 (A,B) 3,4 = C 2,3(C) y3(C,D) B D 2,1 = C 3,2(C) y2(B,C) 4,3 = A 1,4(A) y4(A,D) 2: B,C 3: C,D C 2,3 = B 1,2(B) y2(B,C) 3,2 = D 4,3(D) y3(C,D)
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Loopy BP Run P(a1) Iteration # True posterior 0.85 0.8 0.75 0.7 0.65
0.55 5 10 15 20 Iteration #
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Grid Cluster Graph A1,1 A1,1 , A1,2 A1,2 A1,2 , A1,3 A1,3 A1,1 , A2,1
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Cluster Graphs 1: A, B, C C 3: B,D,F B D E 2: B, C, D 5: D, E 4: B, E
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Loopy in Practice Synchronous BP: all messages are updated in parallel
asynchronous order 1 Time (seconds) 2 4 6 8 10 12 14 # messages converged Ising Grid x 100 asynchronous order 2 synchronous 11 11 12 12 13 13 21 21 22 22 23 23 31 31 32 32 33 33 Asynchronous is faster than synchronous Order of messages has a significant effect on behaviour
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Shannon’s Theorem Goal: Transmit bits over a noisy channel
How efficient can we make our transmission, for arbitrarily low probability of making an error Shannon’s result: Define Channel Capacity = bound on code rate for a given signal to noise ratio all rates under this are achievable, for arbitrarily low error rate (simply make messages long enough) no rate above that is achievable # bits per message Rate of a code = # bits sent
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Y1 Y2 Y3 Y4 X1 X2 X3 X4 U1 U2 U3 U4 X5 X6 X7 Y5 Y6 Y7
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U1 U2 Un Z1 Z2 Zn X1 X2 Xn Y1 Y2 Yn
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Permuter Y4 Y8 X4 X8 W1 W2 W3 W4 U1 U2 U3 U4 Y1 Y3 Y5 Y7 Z1 Z2 Z3 Z4
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Coding: Post 1993 Shannon limit = -0.79
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