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Readings: K&F: 11.3, 11.5 Yedidia et al. paper from the class website

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1 Readings: K&F: 11.3, 11.5 Yedidia et al. paper from the class website Chapter 9 - Jordan Loopy Belief Propagation Generalized Belief Propagation Unifying Variational and GBP Learning Parameters of MNs Graphical Models – 10708 Carlos Guestrin Carnegie Mellon University November 10th, 2006

2 More details on Loopy BP
Difficulty SAT Grade Happy Job Coherence Letter Intelligence Numerical problem: messages < 1 get multiplied together as we go around the loops numbers can go to zero normalize messages to one: Zi!j doesn’t depend on Xj, so doesn’t change the answer Computing node “beliefs” (estimates of probs.): – Carlos Guestrin 2006

3 An example of running loopy BP
– Carlos Guestrin 2006

4 (Non-)Convergence of Loopy BP
Loopy BP can oscillate!!! oscillations can small oscillations can be really bad! Typically, if factors are closer to uniform, loopy does well (converges) if factors are closer to deterministic, loopy doesn’t behave well One approach to help: damping messages new message is average of old message and new one: often better convergence but, when damping is required to get convergence, result often bad – Carlos Guestrin 2006 graphs from Murphy et al. ’99

5 Loopy BP in Factor graphs
What if we don’t have pairwise Markov nets? Transform to a pairwise MN Use Loopy BP on a factor graph Message example: from node to factor: from factor to node: A B C D E ABC ABD BDE CDE – Carlos Guestrin 2006

6 Loopy BP in Factor graphs
From node i to factor j: F(i) factors whose scope includes Xi From factor j to node i: Scope[j] = Y[{Xi} A B C D E ABC ABD BDE CDE – Carlos Guestrin 2006

7 What you need to know about loopy BP
Application of belief propagation in loopy graphs Doesn’t always converge damping can help good message schedules can help (see book) If converges, often to incorrect, but useful results Generalizes from pairwise Markov networks by using factor graphs – Carlos Guestrin 2006

8 Announcements Monday’s special recitation
Pradeep Ravikumar on exciting new approximate inference algorithms – Carlos Guestrin 2006

9 Loopy BP v. Clique trees: Two ends of a spectrum
DIG GJSL HGJ CD GSI Difficulty SAT Grade Happy Job Coherence Letter Intelligence – Carlos Guestrin 2006

10 Generalize cluster graph
Generalized cluster graph: For set of factors F Undirected graph Each node i associated with a cluster Ci Family preserving: for each factor fj 2 F, 9 node i such that scope[fi]µ Ci Each edge i – j is associated with a set of variables Sij µ Ci Å Cj – Carlos Guestrin 2006

11 Running intersection property
(Generalized) Running intersection property (RIP) Cluster graph satisfies RIP if whenever X2 Ci and X2 Cj then 9 one and only one path from Ci to Cj where X2Suv for every edge (u,v) in the path – Carlos Guestrin 2006

12 Examples of cluster graphs
– Carlos Guestrin 2006

13 Two cluster graph satisfying RIP with different edge sets
– Carlos Guestrin 2006

14 Generalized BP on cluster graphs satisfying RIP
Initialization: Assign each factor  to a clique (), Scope[]µC() Initialize cliques: Initialize messages: While not converged, send messages: Belief: – Carlos Guestrin 2006

15 Cluster graph for Loopy BP
Difficulty SAT Grade Happy Job Coherence Letter Intelligence – Carlos Guestrin 2006

16 What if the cluster graph doesn’t satisfy RIP
– Carlos Guestrin 2006

17 Region graphs to the rescue
Can address generalized cluster graphs that don’t satisfy RIP using region graphs: Yedidia et al. from class website Example in your homework!  Hint – From Yedidia et al.: Section 7 – defines region graphs Section 9 – message passing on region graphs Section 10 – An example that will help you a lot!!!  – Carlos Guestrin 2006

18 Revisiting Mean-Fields
Choice of Q: Optimization problem: – Carlos Guestrin 2006

19 Interpretation of energy functional
Exact if P=Q: View problem as an approximation of entropy term: – Carlos Guestrin 2006

20 Entropy of a tree distribution
Difficulty SAT Grade Happy Job Coherence Letter Intelligence Entropy term: Joint distribution: Decomposing entropy term: More generally: di number neighbors of Xi – Carlos Guestrin 2006

21 Loopy BP & Bethe approximation
Difficulty SAT Grade Happy Job Coherence Letter Intelligence Energy functional: Bethe approximation of Free Energy: use entropy for trees, but loopy graphs: Theorem: If Loopy BP converges, resulting ij & i are stationary point (usually local maxima) of Bethe Free energy! – Carlos Guestrin 2006

22 GBP & Kikuchi approximation
DIG GJSL HGJ CD GSI Exact Free energy: Junction Tree Bethe Free energy: Kikuchi approximation: Generalized cluster graph spectrum from Bethe to exact entropy terms weighted by counting numbers see Yedidia et al. Theorem: If GBP converges, resulting Ci are stationary point (usually local maxima) of Kikuchi Free energy! Difficulty SAT Grade Happy Job Coherence Letter Intelligence – Carlos Guestrin 2006

23 What you need to know about GBP
Spectrum between Loopy BP & Junction Trees: More computation, but typically better answers If satisfies RIP, equations are very simple General setting, slightly trickier equations, but not hard Relates to variational methods: Corresponds to local optima of approximate version of energy functional – Carlos Guestrin 2006

24 Learning Parameters of a BN
D S G H J C L I Log likelihood decomposes: Learn each CPT independently Use counts – Carlos Guestrin 2006

25 Log Likelihood for MN Log likelihood of the data: Difficulty SAT Grade
Happy Job Coherence Letter Intelligence – Carlos Guestrin 2006

26 Log Likelihood doesn’t decompose for MNs
A convex problem Can find global optimum!! Term log Z doesn’t decompose!! Difficulty SAT Grade Happy Job Coherence Letter Intelligence – Carlos Guestrin 2006

27 Derivative of Log Likelihood for MNs
Difficulty SAT Grade Happy Job Coherence Letter Intelligence – Carlos Guestrin 2006

28 Derivative of Log Likelihood for MNs
Difficulty SAT Grade Happy Job Coherence Letter Intelligence Derivative: Setting derivative to zero Can optimize using gradient ascent Let’s look at a simpler solution – Carlos Guestrin 2006

29 Iterative Proportional Fitting (IPF)
Difficulty SAT Grade Happy Job Coherence Letter Intelligence Setting derivative to zero: Fixed point equation: Iterate and converge to optimal parameters Each iteration, must compute: – Carlos Guestrin 2006

30 What you need to know about learning MN parameters?
BN parameter learning easy MN parameter learning doesn’t decompose! Learning requires inference! Apply gradient ascent or IPF iterations to obtain optimal parameters – Carlos Guestrin 2006

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