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Learning LiNGAM based on data with more variables than observations
LiNGAM based variables observations
2012/9/17
A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory net-works with no prior knowledge of causal connectivity. Many m...
We consider an adversarial online learning setting where a decision maker can choose an action in every stage of the game. In addition to observing the reward of the chosen action, the decision maker ...
A note on sensitivity of principal component subspaces and the efficient detection of influential observations in high dimensions
distance between subspaces influential observations perturbation principal component analysis
2009/9/16
In this paper we introduce an influence measure based on second order expansion of the RV and GCD measures for the comparison between unperturbed and perturbed eigenvectors of a symmetric matrix estim...
Spatial modelling for mixed-state observations
Multivariate analysis Distribution theory Mixed-state variables Spatial cooperation Markov random fields
2009/9/16
In several application fields like daily pluviometry data modelling, or motion analysis from image sequences, observations contain two components of different nature. A first part is made with discret...