Title: What Info-Metrics and Information Theory Brings to Modeling and Inference
Abstract: The available information is usually too complex and insufficient to deliver a unique solution for most modeling and inference problems. Within a constrained optimization setup, info-metrics, in conjunction with information theory, provides us with a way to sort and rank solutions and choose the one that satisfies our desired properties. It provides us with a different way of thinking about solving such problems and a way to nest models in terms of the information and the decision criterion they use. It also provides new insights into basic modeling and solves inference problems that cannot be solved with conventional methods. In this talk I discuss the info-metrics paradigm for modeling and inference. I briefly examine key classical inferential methods and the way each one of them fits within that paradigm. I also show the benefits of combining classical and information-theoretic econometric modeling.
Title: Some computational methods of information analysis and clustering
Abstract: This lecture addresses the study of information using advanced computational techniques. The talk is divided into two parts. The first analyses COVID19 outbreak using the genetic code of several virus including variants of the SARS-CoV-2. The second part analyses paintings during seven centuries, for a number of artists and styles. In the two cases, hierarchical clustering and the multidimensional scaling, associated with the concepts of entropy and complexity, allow not only an insightful visualization of the embedded information, but also the extension of concepts usual in dynamical systems.
Homepage: http://ave.dee.isep.ipp.pt/~jtm/
Title: Uncertainty in Official Statistics: key perspectives of the Info-Metrics approach
Abstract: Official economic statistics are uncertain even if not always adequately treated. The importance of measuring the uncertainties is very important for administrative and big datasets in contexts relevant for official economic statistics.
Measuring uncertainty is a complex and challenging task and our aim is to encourage further work into the measurement of data uncertainty within an Info-Metrics framework. Information Theoretic based methods of incorporating data uncertainty are introduced and proposed.
Homepage: https://www.unibo.it/sitoweb/rossella.bernardini/en
Title: The Theory of Information and its Value
Abstract: This talk will present Statronovich Theory of the value of information and present applications to machine learning.
Homepage: https://www.ece.ufl.edu/people/faculty/jose-c-principe/