1. Learning Dynamic Bayesian Networks with Changing Dependencies Allan Tucker (allan.tucker@brunel.ac.uk) Xiaohui Liu (xiaohui.liu@brunel.ac.uk) IDA 2003

2. Contents of Talk Introduction to BNs and DBNs Changing Dependencies and the DCCF Datasets HCHC (representation and algorithm) Results (synthetic and oil refinery data) Sample Explanations Conclusions and Future Work IDA 2003

3. BNs and DBNs IDA 2003

4. Changing Dependencies Many examples of MTS with changing dependencies (engineering, medicine) Need to avoid averaging IDA 2003

5. Dynamic Cross Correlation Fn Explores how the CCF varies over time Uses a moving window over a MTS IDA 2003

6. The Datasets Oil Refinery Data Subset of 21 variables over 10000 minutes Synthetic Data IDA 2003

7. Representation Use of hidden controller nodes Inserted as a parent of each variable IDA 2003

8. Hidden Controller Hill Climb IDA 2003

9. The Experiments Synthetic Comparison of HCHC and SEM Structural Difference, DCCF analysis Oil Refinery DCCF analysis Sample Explanations Explorations IDA 2003

10. Results Log Likelihood scores much higher for SEM than HCHC but this could be due to overfitting SD analysis appears to confirm this IDA 2003

11. Results - Synthetic IDA 2003

12. Results – Oil Refinery Data Segmentations appear to differentiate between different dependency structures But also spurious segmentations (non-pairwise relationships?) IDA 2003

13. Explanations 1 IDA 2003

14. Explanations 2 IDA 2003

15. Conclusions Developed a DBN representation and algorithm (HCHC) for learning models from MTS with changing dependencies Synthetic data implies a better model is learnt than using SEM Explanations generated from oil refinery data including the controller nodes IDA 2003

16. Future Work Improve upon SEM (annealing?) Experiment with other datasets Gene Expression Visual Field Continuous DBNs with discrete controller nodes: Hybrid networks IDA 2003

17. Any Questions? IDA 2003