Found 6 relevant results in 4.77s where lecturer="Jonas Peters"
In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference.
We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.
Probability and Statistics
Probability and Statistics
Wahrscheinlichkeit und Statistik
- Diskrete Wahrscheinlichkeitsräume- Stetige Modelle- Grenzwertsätze- Einführung in die Statistik
When learning how to predict a response variable from some covariates, we often assume that training and test distributions are equal. This may not be satisfied in practice, however. In this seminar, we study the problem of distribution generalization, which allows training and test distributions to differ. We consider statistical models, methods and algorithms, and theoretical results.