parameter related domain knowledge for learning in bayesian networks

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Parameter Related Domain Knowledge for

Learning in Bayesian Networks

Stefan NiculescuPhD Candidate, Carnegie Mellon University

Joint work with professor Tom Mitchell and Dr. Bharat Rao

April 2005

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Domain Knowledge

• In real world, often data is too sparse to allow building of an accurate model

• Domain knowledge can help alleviate this problem

• Several types of domain knowledge:– Relevance of variables (feature selection) – Conditional Independences among variables– Parameter Domain Knowledge

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Parameter Domain Knowledge

• In a Bayes Net for a real world domain:– can have huge number of parameters– not enough data to estimate them accurately

• Parameter Domain Knowledge constraints: – reduce the number of parameters to estimate– reduce the variance of parameter estimates

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Outline

• Motivation

Parameter Related Domain Knowledge

• Experiments

• Related Work

• Summary / Future Work

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Parameters and Counts

CPT for variable Xi

Theorem. The Maximum Likelihood estimators are given by:

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Parameter Sharing

1g

11lc

12lc

13lc

1 11

1c 3c2c

1g1g Theorem. The Maximum Likelihood

estimators are given by:

21lc

22lc

23lc

2g 2g 2g32lc

},,{ 3211 cccC

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Incomplete Data, Frequentist

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Dependent Dirichlet Priors

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Bayesian Averaging

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Hierarchical Parameter Sharing

1

1

1 11 11

11 1 1 1

2 2 2

3 3 3

44 4

5 5

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Probability Mass Sharing

DK: Parameters of a given color have the same sum across all distributions.

11 1

k5

...

1211 k1

21 k222

51 5241 k442

kk 2122122111 ...

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Probability Ratio Sharing

11 1

k5

DK: Parameters of a given color preserve their relative ratios across all distributions.

...

k

k

2

1

22

12

21

11 ...

1211 k1

21 k222

51 5241 k442

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Where are we right now?

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Outline

• Motivation

• Parameter Related Domain Knowledge

Experiments

• Related Work

• Summary / Future Work

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Datasets

• Project World - CALO– 6 persons, ~ 200 emails – Manually labeled as About / Not About Meetings– Data: (Person, Email, Topic)

• Artificial Datasets– Kept most of the characteristics of the data BUT ...– ... new emails were generated where frequencies of

certain words were shared across users– Purpose:

• Domain Knowledge readily available• To be able to study the effect of training set size (up to 5000)• To be able to compare our estimated distribution to the true

distribution

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Approach

• Can model Email using a Naive Bayes model:– Without Parameter Sharing (PSNB)

– With Parameter Sharing (SSNB)

• Also compare with a model that assumes the sender is irrelevant (GNB)– the frequencies of words within a

topic to be learnt from all examples

Sender

Word

TopicSender

Word

Topic

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Effect of Training Set Size

As expected:

• SSNB performs better than both models

• SSNB and PSNB tend to perform similarly when the size of training set increases, but SSNB much better when data is sparse

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Outline

• Motivation

• Parameter Related Domain Knowledge

• Experiments

Related Work

• Summary / Future Work

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Dirichlet Priors in a Bayes Net

Prior Belief

Spread

The Domain Expert specifies an assignment of parameters. However, leaves room for some error (Spread)

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HMMs and DBNs

1tX 1tXtX

1tY tY 1tY

... ...

... ...

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Module NetworksIn a Module:

• Same parents

• Same CPTs

Image from “Learning Module Networks” by Eran Segal and Daphne Koller

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Context Specific Independence

Alarm

Set Burglary

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Outline

• Motivation

• Parameter Related Domain Knowledge

• Experiments

• Related Work

Summary / Future Work

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Summary

• Parameter Related Domain Knowledge is needed when data is scarce

• Developed methods to estimate parameters:

– For each of four types of Domain Knowledge presented

– From both complete and incomplete Data

• Markov Models, Module Nets, Context Specific Independence – particular

cases of our parameter sharing domain knowledge

• Models using Parameter Sharing performed better than two classical Bayes

Nets on synthetic data

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Future Work

• Automatically find Shared Parameters

• Study interactions among different types of Domain Knowledge

• Incorporate Domain Knowledge about continuous variables

• Investigate Domain Knowledge in the form of inequality constraints

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Questions ?

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THE END

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Backup Slides

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Hierarchical Parameter Sharing

}{},,,,,,{ 1654321 cccccc

},{},,,{ 32321 ccc }{},,,{ 4654 ccc

}{},,{ 521 cc

{...}},{ 1c {...}},{ 2c

{...}},{ 3c {...}},{ 4c {...}},{ 5c {...}},{ 6c

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Full Data Observability, Frequentist

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Probability Mass Sharing

21

1

)|(1 EnglishWordPc

)|(2 SpanishWordPc

11

1

12 2

2

1415

2425

•Want to model P(Word|Language)

•Two languages: English, Spanish

•Different sets of words

•Domain Knowledge:

•Aggregate Probability Mass of Nouns the same in both

•Same holds for adjectives, verbs, etc

NounsT 1

VerbsT 2

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Probability Mass Sharing

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Full Data Observability, Frequentist

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Probability Ratio Sharing

21

1

)|(1 EnglishWordPc

)|(2 SpanishWordPc

11

1

12 2

2

1415

2425

•Want to model P(Word|Language)

•Two languages: English, Spanish

•Different sets of words

•Domain Knowledge:

•Word groups:

•About computers: computer, mouse, monitor, etc

•Relative frequency of “computer” to “mouse” same in both languages

•Aggregate mass can be different

T1 Computer Words

T2 Business Words

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Probability Ratio Sharing

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Full Data Observability, Frequentist