Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Variational Learning for Switching State-Space Models
Neural Computation
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Knowledge and Information Systems
Making time: pseudo time-series for the temporal analysis of cross section data
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
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Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.