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This course aims to provide a basic understanding of selected Mathematical concepts that many branches of science relies on. The topics will be explained at an abstract, post graduate level.
In this course we will concentrate on the study and practice of info-metrics modeling and inference. We will concentrate on estimation and inference of problems where the information we have is quite limited and often very noisy. Though similar problems arise across most disciplines, we will focus on the study of Information-Theoretic (IT) methods of inference in general (within an interdisciplinary perspective) but with a strong emphasis on problems in the social sciences and economics.
We will emphasize both the fundamental theory, the motivation for using the theory, its background, and practice the theory with real or artificial data. Part of the lectures will be complemented with computer experiment in class. We will compare the info-metrics framework with other methods, like the maximum likelihood and least squares.
For further background a web support with many example, references, codes and software is available. See: http://info-metrics.org/
The course is beneficial to graduate students, researchers and academics from across disciplines with an interest in solving all types of empirical problems with complicated data and with minimal imposed structure and statistical assumptions. Examples include modelling and inference of problems with small, ill-behaved or complex data.
After a successful completion of this course participants are expected to be able to:
The background needed for the course is statistics and/or econometrics traditionally studied during the first year of graduate school in any quantitative discipline.