__Table of Contents__
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__Bayesian Networks and
Decision-Theoretic Reasoning
for Artificial Intelligence
Overview
Science- AAAI-97
Applications
Teenage Bayes
Course Contents
Probabilities
Discrete Random Variables
Continuous Random Variable
More Probabilities
Rules of Probability
Bayes Rule
Course Contents
Bayesian networks
Bayesian Networks
Product Rule
Marginalization
Bayes Rule Revisited
A Bayesian Network
Independence
Conditional Independence
More Conditional Independence:
Naïve Bayes
Naïve Bayes in general
More Conditional Independence:
Explaining Away
Put it all together
General Product (Chain) Rule
for Bayesian Networks
Conditional Independence
Another non-descendant
Independence and Graph Separation
Bayesian networks
Nodes as functions
PPT Slide
Causal Independence
Fine-grained model
Noisy-Or model
PPT Slide
Context-specific Dependencies
Asymmetric dependencies
Asymmetric Assessment
Continuous variables
Gaussian (normal) distributions
Gaussian networks
Composing functions
PPT Slide
Bayesian Networks
What is a variable?
Clarity Test:
Knowable in Principle
Structuring
Do the numbers really matter?
Bayesian Networks and Structure
Course Contents
Inference
Predictive Inference
Combined
Explaining away
Inference in Belief Networks
Basic Inference
Product Rule
Marginalization
Basic Inference
Inference in trees
Polytrees
The problem with loops
The problem with loops contd.
Variable elimination
Inference as variable elimination
Variable Elimination with loops
Join trees*
Exploiting Structure
Noisy-or decomposition
Inference with continuous variables
Computational complexity
Stochastic simulation
Likelihood weighting
Other approaches
PPT Slide
Course Contents
Decision making
Decision making
A Decision Problem
Value Function
Preference for Lotteries
Desired Properties for Preferences over Lotteries
Expected Utility
Some properties of U
Attitudes towards risk
Are people rational?
Maximizing Expected Utility
Multi-attribute utilities
(or: Money isn’t everything)
Influence Diagrams
Decision Making with Influence Diagrams
Value-of-Information
Value-of-Information in an
Influence Diagram
Value-of-Information is the increase in Expected Utility
Course Contents
Learning networks from data
The learning task
Parameter learning: one variable
Maximum likelihood
Bayesian approach
Conditioning on data
PPT Slide
General parameter learning
Partially observable data
Intuition
Expectation Maximization (EM)
Structure learning
Search space
Heuristic search
Scoring
Better scoring functions
Hidden variables
Randomly scattered data
Actual data
Bayesian clustering (Autoclass)
Clustered distributions
Detecting hidden variables
Course Contents
Reasoning over time
Dynamic environments
Dynamic Bayesian networks
Hidden Markov model
Hidden Markov models (HMMs)
HMMs and DBNs
Acting under uncertainty
Partially observable MDPs
Structured representation
Causality
Causal Theory
Setting vs. Observing
Predicting the effects of
interventions
Mechanism Nodes
Persistence
Course Contents
Applications
Why use Bayesian Networks?
Pathfinder
Studies of Pathfinder Diagnostic Performance
Commercial system: Integration
On Parenting: Selecting problem
On Parenting : MSN
Single Fault approximation
On Parenting: Selecting problem
Performing diagnosis/indexing
RICOH Fixit
FIXIT: Ricoh copy machine
Online Troubleshooters
Define Problem
Gather Information
Get Recommendations
Vista Project: NASA Mission Control
Costs & Benefits of Viewing Information
Status Quo at Mission Control
Time-Critical Decision Making
Simplification: Highlighting Decisions
Simplification: Highlighting Decisions
Simplification: Highlighting Decisions
What is Collaborative Filtering?
Bayesian Clustering for Collaborative Filtering
Applying Bayesian clustering
MSNBC Story clusters
Top 5 shows by user class
Richer model
What’s old?
What’s new?
Some Important AI Contributions
What’s in our future? |