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On the Power of Belief Propagation: A Constraint Propagation PerspectivePowerPoint Presentation

On the Power of Belief Propagation: A Constraint Propagation Perspective

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On the Power of Belief Propagation: A Constraint Propagation Perspective

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On the Power of Belief Propagation: A Constraint Propagation Perspective

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On the Power of Belief Propagation:A Constraint Propagation Perspective

Rina Dechter

BozhenaBidyuk

RobertMateescu

EmmaRollon

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How many people?

Causal support

Diagnostic support

- Pearl (1988): use of BP to loopy networks
- McEliece, et. Al 1988: IBP’s success on coding networks
- Lots of research into convergence … and accuracy (?), but:
- Why IBP works well for coding networks
- Can we characterize other good problem classes
- Can we have any guarantees on accuracy (even if converges)

- Sound
- Incomplete
- Always converges
(polynomial)

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DFG

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DFG

Flat constraint network

Belief network

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Updated relation:

Updated belief:

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DFG

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- Theorem:
Iterative BP performs arc-consistency on the flat network.

- Soundness:
- Inference of zero beliefs by IBP converges
- All the inferred zero beliefs are correct

- Incompleteness:
- Iterative BP is as weak and as strong as arc-consistency

- Continuity Hypothesis: IBP is sound for zero - IBP is accurate for extreme beliefs? Tested empirically

Algorithms:

IBP

IJGP

We investigated empirically if the results for zero beliefs extend to ε-small beliefs (ε > 0)

- Measures:
- Exact/IJGP histogram
- Recall absolute error
- Precision absolute error

- Network types:
- Coding
- Linkage analysis*
- Grids*
- Two-layer noisy-OR*
- CPCS54, CPCS360

YES

Have determinism?

NO

* Instances from the UAI08 competition

N=200, 1000 instances, w*=15

w* = 24

w* = 27

w* = 26

Exact Histogram IJGP Histogram Recall Abs. Error Precision Abs. Error

pedigree1, w* = 21

Absolute Error

Percentage

pedigree37, w* = 30

Absolute Error

Percentage

i-bound = 3

i-bound = 7

- Observed variables break the flow of inference
- IBP is exact when evidence variables form a cycle-cutset

- Unobserved variables without observed descendents send zero-information to the parent variables – it is irrelevant
- In a network without evidence, IBP converges in one iteration top-down

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Observed variables with xtreme priors or xtreme support can nearly-cut information flow:

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Average Error vs. Priors

- IBP converges & sound for zero beliefs
- IBP’s power to infer zeros is as weak or as strong as arc-consistency
- However: inference of extreme beliefs can be wrong.
- Cutset property (Bidyuk and Dechter, 2000):
- Evidence and inferred singleton act like cutset
- If zeros are cycle-cutset, all beliefs are exact
- Extensions to epsilon-cutset were supported empirically.