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Learning Dynamic Bayesian Networks with Changing Dependencies

Learning Dynamic Bayesian Networks with Changing Dependencies. Allan Tucker (allan.tucker@brunel.ac.uk) Xiaohui Liu (xiaohui.liu@brunel.ac.uk). IDA 2003. IDA 2003. Contents of Talk. Introduction to BNs and DBNs Changing Dependencies and the DCCF Datasets

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Learning Dynamic Bayesian Networks with Changing Dependencies

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  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. IDA 2003 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

  3. IDA 2003 BNs and DBNs

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

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

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

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

  8. IDA 2003 Hidden Controller Hill Climb

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

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

  11. IDA 2003 Results - Synthetic

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

  13. IDA 2003 Explanations 1

  14. IDA 2003 Explanations 2

  15. IDA 2003 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

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

  17. IDA 2003 Any Questions?

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