IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning probabilistic networks
The Knowledge Engineering Review
Variational Learning for Switching State-Space Models
Neural Computation
Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
Modeling changing dependency structure in multivariate time series
Proceedings of the 24th international conference on Machine learning
Distribution-free learning of Bayesian network structure in continuous domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Discrete temporal models of social networks
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the sample complexity of learning Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Methodological Review: A review of causal inference for biomedical informatics
Journal of Biomedical Informatics
Dynamic bayesian network modeling of cyanobacterial biological processes via gene clustering
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Hidden Source Behavior Change Tracking and Detection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
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Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional DBN structure learning is that the data are generated by a stationary process, an assumption that is not true in many important settings. In this paper, we introduce a new class of graphical model called a non-stationary dynamic Bayesian network, in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. Non-stationary dynamic Bayesian networks represent a new framework for studying problems in which the structure of a network is evolving over time. Some examples of evolving networks are transcriptional regulatory networks during an organism's development, neural pathways during learning, and traffic patterns during the day. We define the non-stationary DBN model, present an MCMC sampling algorithm for learning the structure of the model from time-series data under different assumptions, and demonstrate the effectiveness of the algorithm on both simulated and biological data.