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» Methods for Quantifying the Causal Structure of bivariate Ti...
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KDD
2009
ACM
230views Data Mining» more  KDD 2009»
13 years 11 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
RECOMB
2009
Springer
14 years 7 months ago
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series
Abstract. Understanding the regulatory mechanisms that are responsible for an organism's response to environmental changes is an important question in molecular biology. A fir...
Oliver Stegle, Katherine J. Denby, David L. Wild, ...
KDD
2007
ACM
209views Data Mining» more  KDD 2007»
14 years 6 months ago
Temporal causal modeling with graphical granger methods
The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data...
Andrew Arnold, Yan Liu, Naoki Abe
ICDM
2009
IEEE
152views Data Mining» more  ICDM 2009»
13 years 4 months ago
A Sparsification Approach for Temporal Graphical Model Decomposition
Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct te...
Ning Ruan, Ruoming Jin, Victor E. Lee, Kun Huang