This course introduces the principles of model identification for non-linear dynamic systems, and provides a set of possible solution methods that are thoroughly characterized in terms of modelling assumptions and uncertainty levels. ...
This course will cover mathematical concepts and efficient numerical methods for geometric computing. We will explore the beauty of geometry and develop algorithms to simulate and optimize 2D and 3D geometric models with an emphasis towards computational d ...
The course provides an introduction to econometrics for economics and financial applications. The objective is to learn how to make valid (i.e., causal) inference from economic and social data. ...
Théorie de l'estimation: estimateurs du maximum de vraisemblance, information de Fisher, inégalité de Cramer-Rao, intervalles de confiance.Tests d'hypothèses: cadre de Neyman-Pearson: test du rapport de vraisemblance, tests paramétriques et non-parmétriq ...
Nonparametric models are used to identify a wide range of relationships within data. This course gives a graduate-level overview of nonparametric statistical estimation and inference theory. ...
This course will provide an introduction to model category theory, which is an abstract framework for generalizing homotopy theory beyond topological spaces and continuous maps. We will study numerous examples of model categories and their applications in ...