Learning Dynamic Bayesian Networks with Changing Dependencies Allan Tucker Xiaohui Liu 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 IDA 2003

BNs and DBNs IDA 2003

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

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

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

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

Hidden Controller Hill Climb IDA 2003

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

Results - Synthetic IDA 2003

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

Explanations 1 IDA 2003

Explanations 2 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 IDA 2003

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

Any Questions? IDA 2003