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Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highl ...
In multiple testing, a variety of control metrics have been introduced such as the family-wise error rate (FWER), the False Discovery Rate (FDR), False Exceedence Rate (FER), etc. We found a way to embed these metrics into a continuous family of control me ...
Significance testing is a crucial step in metabolic biomarker recovery from the metabolome-wide latent variables computed by multivariate statistical analysis. In this study we propose an algorithm based on the landscape of the covariance/correlation ratio ...
When many (m) null hypotheses are tested with a single dataset, the control of the number of false rejections is often the principal consideration. Two popular controlling rates are the probability of making at least one false discovery (Bonferroni) and th ...
We present a framework for sparse Gaussian process (GP) methods which uses forward selection with criteria based on information-theoretic principles, previously suggested for active learning. Our goal is not only to learn d-sparse predictors (which can be ...
The Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, is deploying a flexible learning scheme for selected pilot courses in engineering education. In such a scheme, traditional lectures and written exercises are combined with addition ...
