Bayesian Optimal Experimental Design

Titulo:  “Bayesian Optimal Experimental Design”

Ponente: Jesús Fernando López Fidalgo (Universidad de Navarra)

Organizador: Juan Aparicio Baeza

Fecha: Lunes 5 de noviembre de 2018, 12:00 h.

Lugar: Aula 0.1, Instituto Universitario de Investigación CIO, Edificio Torretamarit, Universidad Miguel Hernández (Campus de Elche)

Resumen: A unified view of the topic is presented by putting experimental design in a decision theoretic framework. Experimental design is the only situation where it is meaningful within the Bayesian theory to average over the sample space. As the sample has not yet been observed the general principle of averaging over what is unknown applies. This framework justifies many optimality criteria and opens new possibilities. Various design criteria become part of a single coherent approach. Linear and nonlinear models will be considered as well as a particular application of an optimality criterion for discriminating between any two statistical models in the presence of prior information. If the rival models are not nested then, depending on which model is true, two different Kullback-Leibler distances may be defined.