Table of Contents
Structured Probabilistic Models: Bayesian Networks and Beyond
History
Outline
Probability distributions
Bayesian networks
BN semantics
BN inference (in theory)
BN inference (in practice)
CPCS
Decision Making
Learning
Probabilistic models: applications
Outline
Uncertainty is unavoidable
Build on Strength
Model-based reasoning
Exploit domain structure
Outline
Scaling up
Problem: Size
Problem: Knowledge engineering
Problem: Inference
Large-scale BNs
Exploit structure!
Beyond Bayesian networks
Outline
OOBNs
Inter-object interactions
Probabilistic model
Semantics
Classes
Inheritance
Specific situation models
OOBN inference
Inference: simple approach
Exploit structure
What have we gained�
Abstraction and refinement
Outline
Stochastic dynamic system
Dynamic Bayesian networks
Dynamic OOBNs
Dynamic OOBNs: advantages
What about inference?
DBN inference
What about inference?
Unfortunately not�
Exploit structure
Revealed structure
Outline
Where are we?
Intelligent hospital manager
What do we need?
But we also need �
We need �
OOBNs ? probabilistic frames
Inter-frame dependencies
Structural uncertainty
Number Uncertainty
Reference uncertainty
Structural uncertainty: inference
Exploit structure
Outline
Summary
Exploit structure
Additional pieces
PPT Slide
Recent papers
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