ENBIS Spring Meeting 2018

4 – 6 June 2018; Florence, Italy Abstract submission: 17 November 2017 – 20 April 2018

Keynote: Bayesian Optimal Experimental Design

4 June 2018, 15:00 – 16:10

Abstract

Submitted by
Jesus Lopez-Fidalgo
Authors
Jesus Lopez-Fidalgo (University of Navarra)
Abstract
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.

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