Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
Using Markov Blankets for Causal Structure Learning
The Journal of Machine Learning Research
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Detecting the direction of causal time series
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Grouped graphical Granger modeling methods for temporal causal modeling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A partial correlation-based algorithm for causal structure discovery with continuous variables
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
An efficient causal discovery algorithm for linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
MIDAS - an influence diagram for management of mildew in winter wheat
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
Many applications naturally involve time series data and the vector autoregression (VAR), and the structural VAR (SVAR) are dominant tools to investigate relations between variables in time series. In the first part of this work, we show that the SVAR method is incapable of identifying contemporaneous causal relations for Gaussian process. In addition, least squares estimators become unreliable when the scales of the problems are large and observations are limited. In the remaining part, we propose an approach to apply Bayesian network learning algorithms to identify SVARs from time series data in order to capture both temporal and contemporaneous causal relations, and avoid high-order statistical tests. The difficulty of applying Bayesian network learning algorithms to time series is that the sizes of the networks corresponding to time series tend to be large, and high-order statistical tests are required by Bayesian network learning algorithms in this case. To overcome the difficulty, we show that the search space of conditioning sets d-separating two vertices should be a subset of the Markov blankets. Based on this fact, we propose an algorithm enabling us to learn Bayesian networks locally, and make the largest order of statistical tests independent of the scales of the problems. Empirical results show that our algorithm outperforms existing methods in terms of both efficiency and accuracy.