Research School for Socio-Economic and
Natural Sciences of the Environment
Research School for Socio-Economic and
Natural Sciences of the Environment
Agenda

The Foundations of Info-Metrics Information-Theoretic Methods of inference

Date: 06 May 2019 - 10 May 2019
Location: Leeuwenborch, Wageningen University

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/

Target group and learning outcomes

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: 

  • Understand the basic info-metrics framework, its motivation and background and under what conditions to use it; 
  • Construct and use info-metric models for solving applied policy and other problems 
  • Understand the differences between info-metric, maximum likelihood and least squares approaches; 
  • Apply info-metric estimation techniques to real world problems; 
  • Understand the class of Information-Theoretic (IT) methods of inference. 
  • Perform diagnostics and tests of info-metric (and other information-theoretic) methods.

Assumed prior knowledge

The background needed for the course is statistics and/or econometrics traditionally studied during the first year of graduate school in any quantitative discipline.

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