markov networks

Markov Networks

Model DefinitionComparison to Bayes Nets

Inference techniquesLearning Techniques

A

B

C

D Qn: What is the most likely configuration of A&B?

Facto

r say

s a=b

=0But, m

arginal says

a=0;b=1!

Moral: Factors are not marginals!

Although A,B wouldLike to agree, B&CNeed to agree, C&D need to disagreeAnd D&A need to agree.and the latter three haveHigher weights! Mr. & Mrs. Smith example

Okay, you convinced methat given any potentialswe will have a consistentJoint. But given any joint,will there be a potentials I can provide?

Hammersley-Clifford theorem…

We can have potentials on any cliques—not just the maximal ones. So, for example we can have a potential on A in addition to the other four pairwise potentials

Markov Networks• Undirected graphical models

Cancer

CoughAsthma

Smoking

Potential functions defined over cliquesSmoking Cancer Ф(S,C)

False False 4.5

False True 4.5

True False 2.7

True True 4.5

c

cc xZ

xP )(1)(

x c

cc xZ )(

Log-Linear models for Markov Nets

A

B

C

D

Factors are “functions” over their domainsLog linear model consists of Features fi (Di ) (functions over domains) Weights wi for features s.t.

Without loss of generality!

Markov Networks• Undirected graphical models

Log-linear model:

Weight of Feature i Feature i

otherwise0

CancerSmokingif1)CancerSmoking,(1f

5.11 w

Cancer

CoughAsthma

Smoking

iii xfw

ZxP )(exp1)(

Markov Nets vs. Bayes Nets

Property Markov Nets Bayes NetsForm Prod. potentials Prod. potentials

Potentials Arbitrary Cond. probabilities

Cycles Allowed Forbidden

Partition func. Z = ? global Z = 1 localIndep. check Graph separation D-separation

Indep. props. Some Some

Inference MCMC, BP, etc. Convert to Markov

Connection to MCMC: MCMC requires sampling a node given its markov blanket Need to use P(x|MB(x)). For Bayes nets MB(x) contains more nodes than are mentioned in the local distribution CPT(x) For Markov nets,

Inference in Markov Networks• Goal: Compute marginals & conditionals of

• Exact inference is #P-complete• Most BN inference approaches work for MNs too

– Variable Elimination used factor multiplication—and should work without change..

• Conditioning on Markov blanket is easy:

• Gibbs sampling exploits this

exp ( )( | ( ))

exp ( 0) exp ( 1)i ii

i i i ii i

w f xP x MB x

w f x w f x

1( ) exp ( )i ii

P X w f XZ

exp ( )i i

X i

Z w f X

Gibbs sampling for Markov networks

• Example: P(D | ¬c)• Resample non-evidence

variables in a pre-defined order or a random order

• Suppose we begin with A– B and C are Markov blanket

of A– Calculate P(A | B,C)– Use current Gibbs sampling

value for B & C– Note: never change

(evidence).

A

B C

D E

F

A B C D E F

1 0 0 1 1 0

Example: Gibbs sampling• Resample probability

distribution of A

A

B C

D E

F

?1

?2 ?3

a ¬a

c 1 2

¬c

3 4

a ¬ab 1 5

¬b

4.3 0.2

a ¬a

2 1

A B C D E F

1 0 0 1 1 0? 0 0 1 1 0

F 1 × F 2 × F 3 = a ¬a25.8 0.8

Normalized result = a ¬a0.97 0.03

Example: Gibbs sampling• Resample probability

distribution of B

A

B C

D E

F

?1

?2

?4

d ¬db 1 2

¬b

2 1

a ¬ab 1 5

¬b

4.3 0.2

A B C D E F

1 0 0 1 1 0

1 0 0 1 1 01 ? 0 1 1 0

F 1 × F 2 × F 4 = b ¬b1 8.6

Normalized result = b ¬b0.11 0.89

MCMC: Gibbs Sampling

state ← random truth assignmentfor i ← 1 to num-samples do for each variable x sample x according to P(x|neighbors(x)) state ← state with new value of xP(F) ← fraction of states in which F is true

Learning Markov Networks

• Learning parameters (weights)– Generatively– Discriminatively

• Learning structure (features)• Easy Case: Assume complete data

(If not: EM versions of algorithms)

Entanglement in log likelihood…a b c

Learning for log-linear formulation

Use gradient ascent

Unimodal, because Hessian is Co-variance matrix over features

What is the expectedValue of the feature given the current parameterizationof the network?

Requires inference to answer(inference at every iteration— sort of like EM )

Why should we spend so much time computing gradient?

• Given that gradient is being used only in doing the gradient ascent iteration, it might look as if we should just be able to approximate it in any which way– Afterall, we are going to take a step with some arbitrary

step size anyway..• ..But the thing to keep in mind is that the gradient is a

vector. We are talking not just of magnitude but direction. A mistake in magnitude can change the direction of the vector and push the search into a completely wrong direction…

Generative Weight Learning• Maximize likelihood or posterior probability• Numerical optimization (gradient or 2nd order) • No local maxima

• Requires inference at each step (slow!)

No. of times feature i is true in data

Expected no. times feature i is true according to model

)()()(log xnExnxPw iwiw

i

1( ) exp ( )i ii

P X w f XZ

exp ( )i i

X i

Z w f X

Alternative Objectives to maximize..• Since log-likelihood requires

network inference to compute the derivative, we might want to focus on other objectives whose gradients are easier to compute (and which also –hopefully—have optima at the same parameter values).

• Two options:– Pseudo Likelihood– Contrastive Divergence

Given a single data instance x log-likelihood is

Log prob of data Log prob of all other possible data instances (w.r.t. current q

Maximize the distance (“increase the divergence”)

Pick a sample of typical other instances(need to sample from Pq Run MCMC initializing withthe data..)

Compute likelihood ofeach possible data instancejust using markov blanket (approximate chain rule)

Pseudo-Likelihood

• Likelihood of each variable given its neighbors in the data

• Does not require inference at each step• Consistent estimator• Widely used in vision, spatial statistics, etc.• But PL parameters may not work well for

long inference chains

i

ii xneighborsxPxPL ))(|()(

[Which can lead to disasterous results]

Discriminative Weight Learning

• Maximize conditional likelihood of query (y) given evidence (x)

• Approximate expected counts by counts in MAP state of y given x

No. of true groundings of clause i in data

Expected no. true groundings according to model

),(),()|(log yxnEyxnxyPw iwiw

i

Markov Logic: Intuition

• A logical KB is a set of hard constraintson the set of possible worlds

• Let’s make them soft constraints:When a world violates a formula,It becomes less probable, not impossible

• Give each formula a weight(Higher weight Stronger constraint) satisfiesit formulas of weightsexpP(world)

Markov Logic: Definition• A Markov Logic Network (MLN) is a set of

pairs (F, w) where– F is a formula in first-order logic– w is a real number

• Together with a set of constants,it defines a Markov network with– One node for each grounding of each predicate in the

MLN– One feature for each grounding of each formula F in

the MLN, with the corresponding weight w

Example: Friends & Smokers

habits. smoking similar have Friendscancer. causes Smoking


)()(),(,)()(

ySmokesxSmokesyxFriendsyxxCancerxSmokesx


)()(),(,)()(


1.15.1


)()(),(,)()(


1.15.1

Two constants: Anna (A) and Bob (B)


)()(),(,)()(


1.15.1

Cancer(A)

Smokes(A) Smokes(B)

Cancer(B)



)()(),(,)()(


1.15.1

Cancer(A)

Smokes(A)Friends(A,A)

Friends(B,A)

Smokes(B)

Friends(A,B)

Cancer(B)

Friends(B,B)


Markov Logic Networks• MLN is template for ground Markov nets• Probability of a world x:

• Typed variables and constants greatly reduce size of ground Markov net

• Functions, existential quantifiers, etc.• Infinite and continuous domains

Weight of formula i No. of true groundings of formula i in x

iii xnw

ZxP )(exp1)(

Relation to Statistical Models• Special cases:

– Markov networks– Markov random fields– Bayesian networks– Log-linear models– Exponential models– Max. entropy models– Gibbs distributions– Boltzmann machines– Logistic regression– Hidden Markov models– Conditional random fields

• Obtained by making all predicates zero-arity

• Markov logic allows objects to be interdependent (non-i.i.d.)

Relation to First-Order Logic• Infinite weights First-order logic• Satisfiable KB, positive weights

Satisfying assignments = Modes of distribution• Markov logic allows contradictions between

formulas

MAP/MPE Inference

• Problem: Find most likely state of world given evidence

)|(maxarg xyPy

Query Evidence

MAP/MPE Inference


i

iixy

yxnwZ

),(exp1maxarg

MAP/MPE Inference


i

iiy

yxnw ),(maxarg

MAP/MPE Inference


• This is just the weighted MaxSAT problem• Use weighted SAT solver

(e.g., MaxWalkSAT [Kautz et al., 1997] )• Potentially faster than logical inference (!)

i

iiy

yxnw ),(maxarg

The MaxWalkSAT Algorithm

for i ← 1 to max-tries do solution = random truth assignment for j ← 1 to max-flips do if ∑ weights(sat. clauses) > threshold then return solution c ← random unsatisfied clause with probability p flip a random variable in c else flip variable in c that maximizes ∑ weights(sat. clauses) return failure, best solution found

But … Memory Explosion

• Problem: If there are n constantsand the highest clause arity is c,the ground network requires O(n ) memory

• Solution:Exploit sparseness; ground clauses lazily→ LazySAT algorithm [Singla & Domingos, 2006]

c

Computing Probabilities

• P(Formula|MLN,C) = ?• MCMC: Sample worlds, check formula

holds• P(Formula1|Formula2,MLN,C) = ?• If Formula2 = Conjunction of ground atoms

– First construct min subset of network necessary to answer query (generalization of KBMC)

– Then apply MCMC (or other)• Can also do lifted inference [Braz et al, 2005]

Ground Network Construction

network ← Øqueue ← query nodesrepeat node ← front(queue) remove node from queue add node to network if node not in evidence then add neighbors(node) to queue until queue = Ø

But … Insufficient for Logic

• Problem:Deterministic dependencies break MCMCNear-deterministic ones make it very slow

• Solution:Combine MCMC and WalkSAT→ MC-SAT algorithm [Poon & Domingos, 2006]

Learning

• Data is a relational database• Closed world assumption (if not: EM)• Learning parameters (weights)• Learning structure (formulas)

• Parameter tying: Groundings of same clause

• Generative learning: Pseudo-likelihood• Discriminative learning: Cond. likelihood,

use MC-SAT or MaxWalkSAT for inference

Weight Learning

No. of times clause i is true in data

Expected no. times clause i is true according to MLN

)()()(log xnExnxPw iwiw

i

Structure Learning

• Generalizes feature induction in Markov nets• Any inductive logic programming approach can be

used, but . . .• Goal is to induce any clauses, not just Horn• Evaluation function should be likelihood• Requires learning weights for each candidate• Turns out not to be bottleneck• Bottleneck is counting clause groundings• Solution: Subsampling

Structure Learning

• Initial state: Unit clauses or hand-coded KB

• Operators: Add/remove literal, flip sign• Evaluation function:

Pseudo-likelihood + Structure prior• Search: Beam, shortest-first, bottom-up

[Kok & Domingos, 2005; Mihalkova & Mooney, 2007]

Alchemy

Open-source software including:• Full first-order logic syntax• Generative & discriminative weight learning• Structure learning• Weighted satisfiability and MCMC• Programming language features

alchemy.cs.washington.edu

Alchemy Prolog BUGS

Represent-ation

F.O. Logic + Markov nets

Horn clauses

Bayes nets

Inference Model check- ing, MC-SAT

Theorem proving

Gibbs sampling

Learning Parameters& structure

No Params.

Uncertainty Yes No Yes

Relational Yes Yes No

markov networks

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markov networksexample

markov networksgoal

markov blanket need

markov netsabcd factors

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b wouldlike

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