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# Bayesian Networks Bucket Elimination Algorithm - PowerPoint PPT Presentation

Bayesian Networks Bucket Elimination Algorithm. 主講人：虞台文 大同大學資工所 智慧型多媒體研究室. Content. Basic Concept Belief Updating Most Probable Explanation (MPE) Maximum A Posteriori (MAP). Bayesian Networks Bucket Elimination Algorithm. Basic Concept 大同大學資工所 智慧型多媒體研究室. Satisfiability.

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### Bayesian NetworksBucket Elimination Algorithm

• Basic Concept

• Belief Updating

• Most Probable Explanation (MPE)

• Maximum A Posteriori (MAP)

### Bayesian NetworksBucket Elimination Algorithm

Basic Concept

Given a statement of clauses (in disjunction normal form), the satisfiability problem is to determine whether there exists a truth assignment to make the statement true.

Examples:

Satisfiable

A=True, B=True, C=False, D=False

Satisfiable?

can be true if and only if

can be true.

unsatisfiable

BucketB

BucketC

BucketD

Direct Resolution

Example:

Given a set of clauses

and an order d=ABCD

Set initial buckets as follows:

BucketB

BucketC

BucketD

Direct Resolution

Because no empty clause () is resulted, the statement is satisfiable.

How to get a truth assignment?

BucketB

BucketC

BucketD

Direct Resolution

• Belief updating

• Finding the most probable explanation (mpe)

• Given evidence, finding a maximum probability assignment to the rest of variables.

• Maximizing a posteriori hypothesis (map)

• Given evidence, finding an assignment to a subset of hypothesis variables that maximize their probability.

• Maximizing the expected utility of the problem (meu)

• Given evidence and utility function, finding a subset of decision variables that maximize the expected utility.

• The algorithm will be used as a framework for various probabilistic inferences on Bayesian Networks.

Preliminary – Elimination Functions

Given a function h defined over subset of variables S, where X S,

Eliminate parameterX fromh

Defined overU = S– {X}.

Preliminary – Elimination Functions

Given a function h defined over subset of variables S, where X S,

Preliminary – Elimination Functions

Given function h1,…, hn defined over subset of variables S1,…, Sn, respectively,

Defined over

Preliminary – Elimination Functions

Given function h1,…, hn defined over subset of variables S1,…, Sn, respectively,

### Bayesian NetworksBucket Elimination Algorithm

Belief Updating

Normalization

Factor

C

B

F

D

G

Basic Concept of Variable Elimination

Example:

G(f)

D(a, b)

F(b, c)

B(a, c)

C(a)

BucketG

BucketD

BucketF

BucketB

BucketC

BucketA

BucketG

BucketD

BucketF

BucketB

BucketC

BucketA

0.1

0.7

0.1

0.7

0.1

0.7

0.1

Basic Concept of Variable Elimination

• The BuckElim Algorithm can be applied to any ordering.

• The arity of the function recorded in a bucket

• the numbers of variables appearing in the processed bucked, excluding the bucket’s variable.

• Time and Space complexity is exponentially grow with a function of arity r.

• The arity is dependent on the ordering.

• How many possible orderings for BN’s variables?

C

B

F

D

G

Consider the ordering AFDCBG.

Determination of the Arity

BucketG

BucketB

1

G

4

BucketC

B

1

,3

C

BucketD

0

,2

D

BucketF

,1

0

F

BucketA

0

A

C

B

1

1

F

G

D

4

4

B

G

3

1

C

2

0

D

1

0

F

0

0

A

d

Given the ordering, e.g., AFDCBG.

Determination of the Arity

The width of a graph is the maximum width of its nodes.

w(d) = 4

w*(d) = 4

w(d): width of initial graph

for ordering d.

w*(d): width of induced graph

for ordering d.

Width of node

Width of node

G

B

C

Induced

Graph

D

Initial

Graph

F

A

Goal: Finding an ordering with smallest induced width.

Greedy heuristic and Approximation methods

Are available.

NP-Hard

• The complexity of BuckElim algorithm is dominated by the time and space needed to process a bucket.

• It is time and space is exponential in number of bucket variables.

• Induced width bounds the arity of bucket functions.

C

B

F

D

G

Exercises

• Use BuckElim to evaluate P(a|b=1) with the following two ordering:

• d1=ACBFDG

• d2=AFDCBG

Give the details and make some conclusion.

How to improve the algorithm?

### Bayesian NetworksBucket Elimination Algorithm

Most Probable Explanation (MPE)

Goal:

evidence

Goal:

xi

Notations

Let

Xn

MPE

Some terms involve xn,

some terms not.

Xn is conditioned by its parents.

Xnconditions its children.

Xn

MPE

xnappears in these CPT’s

Not conditioned by xn

Conditioned by xn

Itself

Process the next bucket recursively.

Eliminate variable xnatBucketn.

C

B

F

D

G

Example

C

B

F

D

G

Example

Consider ordering ACBFDG

BucketG

BucketD

BucketF

BucketB

BucketC

BucketA

Consider ordering ACBFDG

### Bayesian NetworksBucket Elimination Algorithm

Maximum

A Posteriori (MAP)

Given a belief network, a subset of hypothesized variablesA=(A1, …, Ak), and evidence E=e, the goal is to determine

C

B

F

D

G

Example

Hypothesis (Decision)

Variables

g = 1

Ordering

Some of them may be observed

Bucket Elimination for MPE

Bucket Elimination for belief updating

C

B

F

g = 1

D

G

Consider orderingCBAFDG

Example

BucketG

BucketD

BucketF

BucketA

BucketB

BucketC

C

B

F

g = 1

D

G

Consider orderingCBAFDG

Exercise

BucketG

BucketD

BucketF

BucketA

Give the detail

BucketB

BucketC