Practically perfect
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Practically Perfect. Chris Meek Max Chickering. X. Y. Perfectness. D-separation implies independence (Pearl, 1988) but… D-connection does not imply dependence. p ( Y =0| X =0) = p ( Y =0| X= 1) (parameter cancellation). X. Z. Y. W. Sampling Local Distributions.

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Practically Perfect

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Practically perfect

Practically Perfect

Chris Meek

Max Chickering


Perfectness

X

Y

Perfectness

D-separation implies independence (Pearl, 1988)

but…

D-connection does not imply dependence

p(Y=0|X=0) = p(Y=0|X=1)

(parameter cancellation)


Sampling local distributions

X

Z

Y

W

Sampling Local Distributions

If we randomly sample local parameters, how

often will the joint be perfect?


Previous result

Previous Result

Probability of sampling non-perfect

distribution is measure zero

(Meek, 1995 and Spirtes et al., 2001)

This Paper

Extend to finite-bit representations:

upper bound on probability of non-perfect

distribution


Why do we care

Why do we care?

  • Previous theoretical result not applicable to real-world software

  • Correctness of learning: typically need perfectness

    Required for “gold standard” experimental

    evaluation of structure learning


Continuous to finite

Continuous to Finite

Sampling

probability

0

1

Renormalized Delta Functions

Sampling

probability

11

00

01

10


Independence polynomial root

X

Y

Independence = Polynomial Root

Base polynomial for “X independent of Y”:

p(X=0,Y=0) · p(X=1,Y=1) – p(X=0,Y=1) · p(X=1,Y=0)

Perfect Polynomial: Enumerate every d-connection in the model, and multiply corresponding base polynomials.

Non-perfect only if perfect polynomial is zero!


Schwartz zippel theorem

Schwartz-Zippel Theorem

  • Every polynomial has a finite number of roots

  • Sample each variable in the polynomial independently from a uniform over fixed set of values

  • Bound on the probability that we sample a root!


Example

Example

For a fixed X, at most three roots (3 roots per column)

For a fixed Y, at most two roots (2 roots per row)

1

Y

0

0

1

X


Problem 1 variational dependence

Problem 1: Variational Dependence

1

p(Y=0)

0

0

1

p(Y=1)

Y has three states: two independent parameters

Theorem does not apply.


Solution change parameterization

Solution: Change Parameterization

1

0

0

1

Sample new parameters independently from Beta distribution

=

Sample “normal” parameters from a Dirichlet distribution


Problem 2 non uniform sampling

Problem 2: Non-uniform Sampling

1

Solution:

Simple Extension to S-Z:

Bound is a function of the maximum density height

0

0

1


Example1

Example

  • b-bit representation

  • Sample parameters from a uniform Dirichlet

  • m variables

  • No variable has more than rmax states

16 vars, 4 states, 64-bit numbers  p(non-perfect) ≤ 1/232


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