1. Explaining Multivariate Time Series to Detect Early Problem Signs Architectures and Efficient Learning Algorithms for Dynamic Bayesian Networks Allan Tucker, Xiaohui Liu

2. Datasets Visual Field & Gene Expression Large/Huge number of variables Short Multivariate Time Series Longitudinal (Experimental Conditions / Patients) Oil Refinery Large Possible Time Lags Changing Dependencies

3. Dynamic Bayesian Networks Probabilistic Graphical Models Easily Used by Non-Statisticians Able to Combine Expert Knowledge with Data Incorporate Hidden / Temporal Nodes etc.

4. 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

5. DBN Architectures

6. Efficient Learning Algorithms Heuristic Grouping Algorithms Seeding Evolutionary Algorithms Intelligent Operators Time Lag Mutation Operators DBN Link Crossover Operators Spatial Crossover and Mutation (VF Data)

7. Some Sample DBNs

8. Explanations

10. 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).

11. The Future Extend Work on DBNs for VF Data Incorporate Expert Knowledge Include more clinical information Classify types of disease from MTS Look into Modelling Continuous Variables Gaussian Networks Continuous BNs