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» Learning Causal Structure from Overlapping Variable Sets
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JMLR
2010
157views more  JMLR 2010»
13 years 2 months ago
Combining Experiments to Discover Linear Cyclic Models with Latent Variables
We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relatio...
Frederick Eberhardt, Patrik O. Hoyer, Richard Sche...
ICML
2005
IEEE
14 years 8 months ago
A causal approach to hierarchical decomposition of factored MDPs
We present Variable Influence Structure Analysis, an algorithm that dynamically performs hierarchical decomposition of factored Markov decision processes. Our algorithm determines...
Anders Jonsson, Andrew G. Barto
CORR
2006
Springer
144views Education» more  CORR 2006»
13 years 7 months ago
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
IEICET
2008
67views more  IEICET 2008»
13 years 7 months ago
Learning of Finite Unions of Tree Patterns with Internal Structured Variables from Queries
We consider the polynomial time learnability of finite unions of ordered tree patterns with internal structured variables, in the query learning model of Angluin (1988). An ordered...
Satoshi Matsumoto, Takayoshi Shoudai, Tomoyuki Uch...
NIPS
1997
13 years 9 months ago
Nonlinear Markov Networks for Continuous Variables
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
Reimar Hofmann, Volker Tresp