Explaining Multivariate Time
Series to Detect Early Problem
Signs
Architectures and Efficient Learning
Algorithms for Dynamic Bayesian Networks
Allan Tucker, Xiaohui Liu
Datasets
Visual Field & Gene Expression
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Large/Huge number of variables
Short Multivariate Time Series
Longitudinal (Experimental Conditions /
Patients)
Oil Refinery
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Large Possible Time Lags
Changing Dependencies
Dynamic Bayesian Networks
Probabilistic Graphical Models
Easily Used by Non-Statisticians
Able to Combine Expert Knowledge with
Data
Incorporate Hidden / Temporal Nodes
etc.
Developing Specialist DBNs
Previously Used DBNs to Generate
Explanations from Oil Refinery Data
Hidden Nodes to Model Changing
Operating Modes
DBN Model to Combine Visual Field MTS
Data with Non-MTS Clinical Data
Combining Gene Expression
Experiments
DBN Architectures
Efficient Learning Algorithms
Heuristic Grouping Algorithms
Seeding Evolutionary Algorithms
Intelligent Operators
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Time Lag Mutation Operators
DBN Link Crossover Operators
Spatial Crossover and Mutation (VF Data)
Variable
Some Sample DBNs
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
-1
-60
-50
-40
-30
Time Lag
-20
-10
0
Explanations
Explanations
Sample of Publications
A Tucker, S Swift and X Liu, "Variable Grouping in Multivariate Time Series
via Correlation", IEEE Transactions on Systems, Man & Cybernetics: Part B:
Cybernetics, 31:235-245, (2001).
A Tucker, X Liu and A Ogden-Swift, “Evolutionary Learning of Dynamic
Probabilistic Models with Large Time Lags”, International Journal of
Intelligent Systems, 16:621-645, (2001).
P Kellam, X Liu, N Martin, C Orengo, S Swift, A Tucker, “A Framework for
Modelling Virus Gene Expression Data”, Intelligent Data Analysis – An
International Journal, Vol. 6, No. 3, IOS Press, Netherlands, pp. 265-280,
(2002).
The Future
Extend Work on DBNs for VF Data
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Incorporate Expert Knowledge
Include more clinical information
Classify types of disease from MTS
Look into Modelling Continuous
Variables
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Gaussian Networks
Continuous BNs