Sciweavers

Share
ESANN
2007

Exploring the causal order of binary variables via exponential hierarchies of Markov kernels

8 years 10 months ago
Exploring the causal order of binary variables via exponential hierarchies of Markov kernels
Abstract. We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n ≥ 4) binary variables X1, . . . , Xn. Our inference principle states that the factorization of the joint probability into conditional probabilities for Xj given X1, . . . , Xj−1 often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule.
Xiaohai Sun, Dominik Janzing
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Xiaohai Sun, Dominik Janzing
Comments (0)
books