DEPARTMENT OF MATHEMATICS, UNIVERSITY OF AVEIRO

OCTOBER 16-17, 2017 – ROOM SOUSA PINTO

13:45 – 14:00

Reception

14:00 – 14:30

**Amos Golan** (Info-Metrics Institute, American University; Santa Fe Institute; Pembroke College, Oxford)

*Info-metrics* is the science and practice of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated.

In my talk, I will very briefly introduce the basic idea, motivation and framework using graphical representations of the problem and the theory behind the solution. I will also provide graphical representations of a number of case studies from across the scientific spectrum.

14:30 – 15:00

**José P. Sande Lemos** (CENTRA, Physics Department, Instituto Superior Ténico, University of Lisbon)

15:00 – 15:30

**Paulo Ferreira** (IEETA, University of Aveiro)

The concepts of entropy and algorithmic complexity are interesting on their own but can also be applied to solve large-scale classification problems in somewhat surprising ways.

The goal of this talk is to discuss how these fundamental concepts can be combined with probabilistic models and data compression techniques to detect in multiple streams of data regions of similarity, regions of uniqueness and rearrangements. The applications include authorship attribution and genomic data analysis.

15:30 – 16:00

Coffee break

16:00 – 16:30

**Elvira Silva** (CEF.UP, University of Porto)

16:30 – 17:00

**Andreia Dionísio** (CEFAGE, University of Évora)

In this communication we intend to show the main advantages (and limitations) of GME facing to OLS procedures to estimate regression models in specific conditions. In order to better show our purposes, we will present two examples: (i) utility function estimation; and (ii) evaluation of financial integration in the European Union.

We compare the performance of the GME estimator with ordinary least square (OLS) in a real data sample setup. The results confirm the ones obtained for samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of regression models, despite the difficulties in computational terms. So, it is worth the effort?

17:00 – 17:30

**Maria Conceição Costa** (CIDMA, University of Aveiro)

09:00 – 10:30

**Amos Golan** (Info-Metrics Institute, American University; Santa Fe Institute; Pembroke College, Oxford)

**Foundations of Info-Metrics — A Tutorial (Information-Theoretic Methods of Inference) **(part I)

10:30 – 11:00

Coffee break

11:00 – 12:30

**Amos Golan** (Info-Metrics Institute, American University; Santa Fe Institute; Pembroke College, Oxford)

**Foundations of Info-Metrics — A Tutorial (Information-Theoretic Methods of Inference) **(part II)

**Note:** This tutorial will be based on the book “*Foundations of Info-Metrics: Modelling, Inference, and Imperfect Information*“.

The support page: **http://www.info-metrics.org**

This workshop is supported in part by the Portuguese Foundation for Science and Technology (FCT – Fundação para a Ciência e Tecnologia), through CIDMA – Center for Research and Development in Mathematics and Applications, within project UID/MAT/04106/2013.