Statistical information: A Bayesian perspective


We explore the concept of information in statistics: information about unknown quantities of interest, the parameters. We discuss intuitive ideas of what should be information in statistics. Our approach on information is divided in two scenarios: observed data and planning of an experiment. On the first scenario, we discuss the Sufficiency Principle, the Conditionality Principle, the Likelihood Principle and their relationship with trivial experiments. We also provide applications of some measures of information to an intuitive example. On the second scenario, the definition and new applications of Blackwell Sufficiency are presented. We discuss a new relationship between Blackwell Equivalence and the Likelihood Principle. Finally, the expected values of some measures of information are calculated.

In Entropy
Rafael B. Stern
Rafael B. Stern
Professor of Statistics

I am an Assistant Professor at the University of São Paulo. I have a B.A. in Statistics from the University of São Paulo, a B.A. in Law from Pontifícia Universidade Católica in São Paulo, and a Ph.D. in Statistics from Carnegie Mellon University. I am currently a member of the Scientific Council of the Brazilian Association of Jurimetrics, an associate investigator at NeuroMat and a member of the Order of Attorneys of Brazil.